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What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 376173 YouTube NJIT
Data Mining Trends and Research Frontiers - Kelompok Bo Cuan Gpp
 
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Video Presentasi Data Mining Trends and Research Frontiers Kelompok Bo Cuan Gpp
Views: 606 Ria Liuswani
Data trends in 2018: 7 things you need to know
 
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We’re on the verge of a data boom—are you taking full advantage of the data you’re collecting? See all trends that will impact your data strategy in 2018: https://www.pluralsight.com/blog/data-professional/data-trends-2018 Visit us at: Facebook: https://www.facebook.com/pluralsight Twitter: https://twitter.com/pluralsight LinkedIn: https://www.linkedin.com/company/pluralsight Instagram: http://instagram.com/pluralsight Blog: https://www.pluralsight.com/thehub
Views: 1029 Pluralsight
Top 10 Global Current Trends in Data Mining 2018
 
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Top 10 Global #Data #Mining trends and predictions 2018 - #Outsourcebigdata provide suggestions for the data mining outsourcing industry, worldwide latest global mining industry overview and mining technology predictions to watch in 2018. To check hottest #dataminingtrends to be aware of in 2018, subscribe our channel at https://goo.gl/znWAEF Follow Us: Facebook - https://www.facebook.com/OutsourceBigData Instagram - https://www.instagram.com/outsourcebigdata/ Twitter - https://twitter.com/OBigdata LinkedIn - https://www.linkedin.com/company/outsource-bigdata/ Google+ - https://plus.google.com/u/0/+Outsourcebigdata Pinterest - https://in.pinterest.com/outsourcebigdata/ YouTube - https://www.youtube.com/user/OutsourceBigData/videos For more detail about data mining future trends contact: https://outsourcebigdata.com outsource data, outsource data entry, outsource data mining
Views: 264 BIG DATA
Introduction to Data Mining for Educational Researchers
 
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Recording of a tutorial held at the second annual Learning Analytics Summer Institute on Data Mining aimed at Educational Researchers.
Views: 1037 Christopher Brooks
Data Mining Marketing Research ChannelAide
 
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http://www.channelaide.com/ marketing research done for your online selling
Views: 194 Mike Gerts
Optimization Software and Systems for Operations Research: Best Practices and Current Trends
 
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Research Seminar by Fourer, Robert on "Optimization Software and Systems for Operations Research: Best Practices and Current Trends". For a great variety of large-scale optimization problems arising in Operations Research applications, it has become practical to rely on "off-the-shelf" software, without any special programming of algorithms. As a result the use of optimization within business systems has grown dramatically in the past decade. One key factor in this success has been the adoption of model-based optimization. Using this approach, an optimization problem is conceived as a particular minimization or maximization of some function of decision variables, subject to varied equations, inequalities, and other constraints on the variables. A range of computer modeling languages have evolved to allow these optimization models to be described in a concise and readable way, separately from the data that determines the size and shape of the resulting problem that may have thousands (or even millions) of variables and constraints. After an optimization problem is instantiated from the model and data, it is automatically put into a standard mathematical form and solved by sophisticated general-purpose algorithmic software packages. Numerous heuristic routines embedded within these packages enable them to adapt to many problem structures without any special effort from the model builder. The evolution and current state of both modeling and solving software for optimization will be presented in the main part of this talk. The presentation will then conclude with a consideration of current trends and likely future directions.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 48 aircc journal
Welche Trends bestimmen 2018 den digitalen Handel? | dotSource Research
 
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Big und smart Data, Hearables, Wearables, Content Commerce und Data Mining - die Buzzword-Schlacht im digitalen Handel geht auch 2018 in eine neue Runde. Handelskraft Digital-Evangelist Oliver Kling erklärt dir in diesem Video, welche Trends 2018 den digitalen Handel bestimmen werden. Trends, Zahlen, Fakten und Brancheninsights gibt es exklusiv für Händler und Hersteller in unserem Digital-Business Kompass Handelskraft 2018 »Vorwärts zur digitalen Exzellenz«. Das Trendbuch kann hier zum kostenlosen Download angefordert werden: http://bit.ly/2CP7aT7 Für aktuelle Trends, Expertenwissen und Klartext statt überstrapazierter Buzzworte sicher dir jetzt dein Ticket für die Handelskraft 2019 am 28. März in der Klassikstadt in Frankfurt am Main: https://bit.ly/2N06c89 Musik: ajstream - AJStream Opening #2 https://soundcloud.com/ajstream/the-stream-opening
Views: 805 dotSource GmbH
IEEE DATAMINING TOPICS - FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS
 
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TSYS Center for Research and Development (TCRD) is a premier center for academic and industrial research needs. We at TRCD provide complete support for final year Post graduate Student (M.E / M.Tech / M. Sc/ MCA/ M-phil) who are doing course in computer science and Information technology to do their final year project and journal work. For Latest IEEE DATA MINING Projects Contact: TSYS Center for Research and Development (TSYS Academic Projects) Ph.No: 9841103123 / 044-42607879, Visit us: http://www.tsys.co.in/ Email: [email protected] IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2016 TOPICS 1. A Simple Message-Optimal Algorithm for Random Sampling from a Distributed Stream 2. Online Learning from Trapezoidal Data Streams 3. Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases 4. CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites 5. Online Subgraph Skyline Analysis over Knowledge Graphs 6. K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis 7. ATD: Anomalous Topic Discovery in High Dimensional Discrete Data 8. Multilabel Classification via Co-evolutionary Multilabel Hypernetwork 9. Learning to Find Topic Experts in Twitter via Different Relations 10. Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables 11. RSkNN: kNN Search on Road Networks by Incorporating Social Influence 12. Unsupervised Visual Hashing with Semantic Assistant for Content-based Image Retrieval 13. A Scalable Data Chunk Similarity based Compression Approach for Efficient Big Sensing Data Processing on Cloud 14. Network Motif Discovery: A GPU Approach 15. Crowdsourced Data Management: A Survey 16. Resolving Multi-Party Privacy Conflicts in Social Media 17. Improving Construction of Conditional Probability Tables for Ranked Nodes in Bayesian Networks 18. Clearing Contamination in Large Networks 19. Private Over-threshold Aggregation Protocols over Distributed Databases 20. Challenges in Data Crowdsourcing 21. Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System
Webinar: The Process Revolution - How Data and Science Are Fueling Next Generation BPM
 
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Michal Rosik and Richard Lipovsky of Minit.io share their knowledge and experience with process mining projects. Watch the webinar to learn: - Why Process Mining is on the rise according to Gartner - Which industries are leading process mining adoption and other trends - Case studies demonstrating real results from implementing process mining - Demo of the comprehensive process analysis capabilities of Minit platform Learn more about Minit platform at www.minit.io
Hidden connections - Data analysis in brain and supermarket
 
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Neuroscientist Sonja Grün uses methods from retailing market research to understand how neurons cooperate.The statistical method known as "frequent itemset mining" (FIM) finds groups of objects in large volumes of data quickly and efficiently and counts their frequencies. In retailing market research, this is used, for example, to identify products that are often purchased together. In brain research, a modified version of the FIM method helps to distinguish behaviour-dependent activity patterns from random patterns. This enabled Jülich scientists to establish which of the simultaneously active neurons form a functional group, for instance while the eye focuses on a given object. (mb) A film by Johannes Faber and Gunnar Grah
TCT SAS Data Mining and Risk Management Technology and Job Market Trends(07).mp4
 
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Mr. Yin Cui is a Manager in Basel Analytic, Risk Management in Scotiabank. He worked as a Portfolio Risk Specialist in CIBC's risk management group before he joined Scotia Bank. Mr. Yin Cui has excellent understanding of retail lending risk management and strong expertise in SAS. He holds a Master Degree in Statistics and has been certified by SAS Institution in various trainings.
Views: 158 TorontoCollegeTCT
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 3 aircc journal
Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014)
 
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The Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014) is jointly organized by AIRCC's Computer Science & Information Technology Community (CSITC) and Vel Tech Dr. RR & Dr. SR Technical University, Chennai, India. Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Data Mining and knowledge management process. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern data mining concepts and establishing new collaborations in these areas. http://airccj.org/2014/dkmp/index.html
Views: 147 IJNGN
McKinsey Careers: An inside look at McKinsey Analytics
 
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Interested in big data, predictive analytics and/or data science? McKinsey has a rapidly growing team tackling tough challenges in this area for clients in all industries and geographies. Peek inside a team room and learn about life at McKinsey from some of our Analytics colleagues.
Views: 36641 McKinsey & Company
New Research Trends in the Field of Data Analytics and Internet of Things (IoT) - Part 01
 
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Informative Speech by Mr. Sandar Ali Khowaja on New Research Trends in the Field of Data Analytics and Internet of Things (IoT) and Its Applications in Healthcare Industry
Views: 32 K D
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 164 ijdkp jou
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 19 aircc journal
Big Data (Introduction for Business Students)
 
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This short revision video introduces the concept of big data. Big data is the process of collecting and analysing large data sets from traditional and digital sources to identify trends and patterns that can be used in decision-making. These large data sets are both structured (e.g. sales transactions from an online store) and unstructured (e.g. posts) on social media. The quantity of data generated is growing exponentially, including data generated by: Retail e-commerce databases User-interactions with websites and mobile apps Usage of logistics, transportation systems, financial and health care Social media data Location data (e.g. GPS-generated) Internet of Things (IoT) data generated New forms of scientific data (e.g. human genome analysis) Some important uses of big data include: Tracking and monitoring the performance, safety and reliability of operational equipment (e.g. data generated by sensors) Generating marketing insights into the needs and wants of customers, based on the transactions, feedback, comments (e.g. from e-commerce analytics, social media posts). Big data is revolutionising traditional market research. Improved decision-making - for example analysing the real-time impact of pricing changes or other elements of the marketing mix (the use of big data to drive dynamic pricing is a great example of this). Better security of business systems: big data can be analysed to identify unusual activity, for example on secure-access systems More efficient management of capacity: the increasing use of big data to inform decision-making about capacity management (e.g, in transportation and logistics systems) is a great example of how big data can help a business operate more efficiently
Views: 2042 tutor2u
International Journal of Data Mining & Knowledge Management Process
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 147 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] ******************************************************************* Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail ijdkpjo[email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 47 aircc journal
International Journal of Data Mining & Knowledge Management Process  IJDKP
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Gene Ekster – The Alternative Data revolution on Wall St
 
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This talk will focus on the role that non-traditional data research, known as alternative data, is beginning to play across the investment community. We will address how datasets such as point of sale transactions, web site usage, municipality records, social media data and similar information are being utilized by traditional long-short funds, quantitative hedge funds and also mutual funds. Topics covered will include aspects of the developing alternative data ecosystem including: * Alternative data R&D process flow * Computing infrastructure and the technology stack * Research & analytics providers * Technical solutions to common issues found in alt. data * Best practices We’re going to walk through a few examples of how noisy, unstructured data become an investable signal using tools such as text mining and machine learning. The aim is to introduce the audience to the process of how hedge fund portfolio managers and sell-side research analysts are systematically generating returns by leveraging unique primary (bots / scrapers, channel checks) and third party datasets (including data brokers). This includes sourcing, compliance, scrubbing out PII, alpha generation related to revenue estimates and approaches to balance the secret sauce with product transparency. Finally, we’ll ponder the future of alternative data in finance and touch on how companies in the data space can best take advantage of this growing trend. Gene Ekster was previously head of R&D at Point72 Asset Management (formerly SAC Capital), a Director of Data Product at 1010Data and a Senior Analyst at Majestic Research (now ITG Investment Research). Currently, Gene works with asset management firms and data providers in a consulting capacity to help integrate alternative data into the investment process. He can be reached via LinkedIn (https://www.linkedin.com/in/geneekster). This talk was recorded at The Fifth Elephant 2016, India's premier data analytics conference.
Views: 2173 HasGeek TV
Where Big Data and Innovation Management are Headed in 2015
 
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Presented by: Huub Rutten, VP Research and Steven Kleynenberg Senior Software Engineer for Sopheon Huub and Steven delve deeper into the topic of Big Data and Innovation Management. In a previous webinar on September 11th 2014 we discussed Big Data at a high level. Now we are giving you the opportunity to learn specific methods around: • How to find white spaces • Generating trends • Predicting topics • Using Big Data to improve your innovation ROI in 2015 Our goal is for participants to learn some basic methodologies around using Big Data for better and more mature Innovation Management Techniques. Our presenters will illustrate these methods with concrete examples, supported by some nice text and data mining software from the Sopheon Research lab. View the entire webinar at: http://budurl.com/bubr
Views: 140 Sopheon
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 13 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 19 aircc journal
Data Mining and Text Mining with John Elder
 
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Analytics 2014 Conference Keynote Conference John Elder of Elder Research explains the top three challenges of data mining and text mining, and how to solve them. Learn more about Analytics 2014 at http://www.sas.com/analyticsseries/us/
Views: 1149 SAS Software
Machine Learning with Small Data Sets in the Age of Deep Learning
 
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Dr. Lei Tang and Dr. Xin Xu will talk about how they apply machine learning with small data sets in sales management and forecast. The recent successes of machine learning and deep learning can be largely attributed to three factors: emergence of abundant data, development of innovative algorithms, and availability of machine learning tools and computing resources. Unfortunately, not all application spaces provide data sets large enough to be used in the usual or obvious ways. In this talk, Lei and Xin focus on one specific domain, enterprise sales, where data is often limited in volume, always noisy, and constantly evolving. They describe how machine learning, and in particular deep learning, can help, and how we address the data challenges described. They specifically discuss how to select model architectures appropriate for these limited data situations, for example, how deep our networks should be. By sifting through sales records and associated sales activities Lei and Xin enable identification of at-risk opportunities as well as project and estimated the time required to close each deal. This, in turn, contributes to the generation of a reliable business forecast for sales managers and executives. Lessons and findings learned through the process is shared. Speaker Bios: Dr. Lei Tang is the Chief Data Scientist at Clari Inc., a startup backed by Sequoia Capital and Bain Capital ventures, focusing on predictive analytics for sales execution and forecasting. Lei received his Ph.D. in computer science from Arizona State University in 2010, and B.S. from Fudan University, China. He is passionate about reshaping variety of businesses, driving business growth and decision through data science and machine learning. From 2012-2014, Lei was the lead data scientist at Demand Generation of @WalmartLabs, where he worked closely with marketing team to drive traffic to site, impacting hundreds of revenue each year. Before that, Lei had 2-year stint at advertising sciences in Yahoo! Labs, working on targeting, user profiling/segmentation by mining user behavioral, social and content information. Lei has co-authored one book on “community detection and mining in social media” (top-download in the corresponding data mining lecture series), held 4 patents, published over 30 papers at top-notch conferences and journals on data mining/machine learning, with over 4000 citations. Dr. Xin Xu is currently working as a data scientist in Clari. Before this, She received her Ph.D degree in Computer Engineering from North Carolina State University in 2015. She also did summer intern in Bell Labs and Akamai Technology in 2014 and 2015 respectively. Her current research interest mainly focuses on applying data mining, machine learning and advanced analytics to solve practical problems in sales domain.
More data management by the buy-side
 
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Marion Leslie, Managing Director, Enterprise, Thomson Reuters, talks about the current buy-side trends from an enterprise data perspective, how customers describe their needs given these market trends, and what customers can do to adapt and evolve in order to drive future success. For all your fintech-related news, please visit https://www.finextra.com.
Views: 30 Finextra Research
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 22 aircc journal
Top 10 Trends In Data Science | Eduonix
 
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Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured, similar to data mining. Data Science is spreading its roots gradually and becoming a hot topic of discussion everywhere. We have made a detailed video, which will tell you about all the recent trends which are going around in Data Science and if you're planning to choose your career in DS you will get a clearer idea about your path. We hope you like this video. The top trends mentioned in the video are: 1. Internet of Things (IoT) 2. Artificial Intelligence 3. Augmented Reality 4. Hyper Personalisation 5. Graph Analytics 6. Machine Intelligence 7. Agile Data Science 8. Behavioral Analytics 9. Journey Sciences 10. The Experience Economy Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: https://goo.gl/ZqRVjS ■ Twitter: https://goo.gl/oRDaji ■ Google+: https://goo.gl/mfPaxx ■ Instagram: https://goo.gl/7f5DUC | @eduonix ■ Linkedin: https://goo.gl/9LLmmJ ■ Pinterest: https://goo.gl/PczPjp
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates **************** Submission Deadline : June 09, 2018 Notification : July 09, 2018 Final Manuscript Due : July 16, 2018 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 3 aircc journal
Using Analytics in Supply Chain Management | Data Analytics in Supply Chain | Great Learning
 
01:11:12
#Analytics | Learn how to use analytics in supply chain management and various ways to solve and optimise complex supply chain issues. Data is being used in every domain to generate valuable insights and make informed business decisions. Great Learning organised a webinar to help participants understand how to maximise business impact from Analytics in the Supply-Chain domain. About the Speaker: Yash Rai is working as a Managing Consultant at The Smart Cube based out of their London office with more than 9 years of extensive experience in the field of analytics and research. Know More: https://goo.gl/731eyd Know More about our analytics courses: PGP-Business Analytics: https://goo.gl/uPAbtV PGP-Big Data Analytics: https://goo.gl/APoZ8Y Business Analytics Certificate Program: https://goo.gl/w3aUCo #SupplyChainManagement #DataAnalytics #AnalyticsInSupplyChain #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 15719 Great Learning
Healthcare Data Mining with Matrix Models (Part 1)
 
01:44:58
Authors: Joel Dudley, Icahn School of Medicine at Mount Sinai Ping Zhang, IBM Thomas J. Watson Research Center Fei Wang, Department of Healthcare Policy and Research, Cornell University Abstract: In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 638 KDD2016 video
19 Industries The Blockchain Will Disrupt
 
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The blockchain is a distributed ledger technology that underlies cryptocurrencies like Bitcoin and platforms like Ethereum. It provides a way to record and transfer data that is transparent, safe, auditable, and resistant to outages. The blockchain has the ability to make the organizations that use it transparent, democratic, decentralized, efficient, and secure. It's a technology that holds a lot of promise for the future, and it is already disrupting many industries. Original post: http://futurethinkers.org/industries-blockchain-disrupt More podcasts & videos about the blockchain: http://futurethinkers.org/blockchain Support us on Patreon: https://www.patreon.com/futurethinkers Check out our merch store: https://www.redbubble.com/people/futurethinkers Blockchain startups and projects featured in this video: Bitcoin - https://bitcoin.org/ Abra - https://www.goabra.com/ Provenance - https://www.provenance.org/ Fluent (Rebranded to Hijro) - https://hijro.com/ SKUChain - https://skuchain.com/ Blockverify - http://www.blockverify.io/ Augur - https://augur.net/ Networking and IoT Adept - http://www.coindesk.com/ibm-reveals-proof-concept-blockchain-powered-internet-things/ Aeternity - https://www.aeternity.com/ Arcade City - https://arcade.city/ La'Zooz - http://www.shareable.net/blog/lazooz-the-decentralized-crypto-alternative-to-uber Innogy - https://bitcoinmagazine.com/articles/innogy-charges-new-electric-car-fleet-using-ethereum-blockchain/ UBS - https://www.ubs.com/microsites/blockchain-report/en/home.html ZF - http://www.econotimes.com/UBS-bank-innogy-and-ZF-partner-to-provide-blockchain-backed-wallets-for-cars-471860 Online Data Storage Storj - https://storj.io/ IPFS - https://ipfs.io/ BitGive Foundation - https://bitgivefoundation.org/ Democracy Earth - http://democracy.earth/ Follow My Vote - https://followmyvote.com/ GovCoin - http://www.businesswire.com/news/home/20160707005803/en/GovCoin-Systems-Implements-Social-Welfare-Payments-Distribution Dubai Blockchain Strategy - http://www.smartdubai.ae/dubai_blockchain.php Circles - aboutcircles.com Gem - https://gem.co/ Tierion - https://tierion.com/ TransactiveGrid - http://transactivegrid.net/ Mycelia - http://myceliaformusic.org/ Ujo Music - https://ujomusic.com/ OpenBazaar - https://www.openbazaar.org/ OB1 - https://ob1.io/ Ubitquity - https://www.ubitquity.io/ Consensys - https://consensys.net/about/ Ethereum - https://www.ethereum.org/ Future Thinkers is a podcast about evolving technology, society and consiousness. If you want to learn more about the blockchain technology, you can listen to some of our episodes going in depth. http://futurethinkers.org/blockchain/ Support us on Patreon: https://www.patreon.com/futurethinkers Donate through Paypal or cryptocurrency: http://futurethinkers.org/support Check out our merch store: https://www.redbubble.com/people/futurethinkers
Views: 1373452 Future Thinkers
Big Data & Analytics for Healthcare
 
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http://ibm.co/healthcareanalytics Healthcare organizations are leveraging the IBM Big Data & Analytics platform to capture all of the information about a patient to get a more complete view for insight into care coordination and outcomes-based reimbursement models, population health management, and patient engagement and outreach. Successfully harnessing big data unleashes the potential to achieve the three critical objectives for healthcare transformation: Build sustainable healthcare systems Collaborate to improve care and outcomes Increase access to healthcare
Views: 59006 IBM Analytics
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 90 Sivakumar Arumugam
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, ducational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 23 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates **************** Submission Deadline : August 05, 2017 Notification : September 05, 2017 Final Manuscript Due : September 13, 2017 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 33 aircc journal
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1546 Quantopian
Introduction to Regression Analysis
 
07:51
This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.
Views: 177436 Mathispower4u
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:13
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 28 aircc journal
Predicative analytics with “text mining” provides line of sight to loss trends.
 
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With advances in data collection and new technologies, predictive analytics have become more and more prevalent among the risk management community. At the 2014 RIMS Conference, Steven Laudermilch, SVP of Claims ACE Claims Group, provided an overview of recent developments in predictive analytics, and offered some advice for risk managers. Mr. Laudermilch says one of the biggest misconceptions about predictive modeling is that building the mode is the more difficult than putting it into place. He acknowledges the expertise and investment required to build models, “putting it to work is the greatest operational challenge that companies face.” He warns companies not to dismiss the output, but accept it as insight to claims. According to Mr. Laudermilch, “about three-quarters of claims represent about 75% of loss costs to an organization.” He says companies “need to find the loss driving segment of claims…intervene on those claims…and you have to drive better outcomes.” Predictive analytics “put a spotlight on claims…to drive business value.” Structured data has a lot of limitations. “Text mining is the ability to access and analyze unstructured data (like claim notes)”, something ACE has invested heavily in. Text mining allows risk managers to sift through claims notes to look for risks that might be emerging…providing “a line of sight into loss trends and experience.” Mr. Laudermilch believes risk managers without predictive analytics should “get onboard quickly.” He suggests they be thoughtful in their adoption. He likens predictive analytics without an organized operational response to an “unused gym membership.” He believes risk managers must embed predictive analytics into the claims process, change the way you intervene on claims, attack severity claims and drive better outcomes.” Companies that do not dot it will be at a competitive disadvantage. For more World Risk and Insurance News from the 2015 RIMS Conference in New Orleans, visit the dedicated RIMS 2015 Channel in the WRIN.tv On Demand Library.
Views: 111 WRINtv
Empirical Mining of Large Data Sets Helps to Solve Practical Large-Scale Forest Management
 
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by Bill Hargrove We present a panoply of examples where empirical mining and statistical analysis of large data sets have already proven useful to help handle vexing problems within the realm of large-scale forest ecology. Some prejudices may exist against empirical approaches, in favor of more process-oriented analytical methods. Because a full understanding and appreciation of particular ecological phenomena are possible only after process-driven, hypothesis-directed research, some forest ecologists may feel that purely empirical data harvesting may represent a less-than-satisfactory approach. Restricting ourselves exclusively to process-driven approaches, however, may result in substantially slower progress, and we may not be able to afford the delays caused by such specific approaches. Empirical methods allow trends, relationships and associations to emerge freely from the data themselves, unencumbered by preconceived theories, ideas and prejudices. Empirical methods can be extremely efficient at uncovering strong correlations with intermediate "linking" variables. Once identified, these correlative structures directly provide sufficient prognostic talent and predictive power to be harnessed by, e.g., Bayesian Belief Nets, which bias ecological management decisions made with incomplete information toward favorable outcomes. Empirical data-harvesting also generates a myriad of testable hypotheses regarding processes, some of which may even be correct. Quantitative statistical regionalizations using Multivariate Geographic Clustering have lended insights into carbon eddy-flux direction and magnitude, wildfire biophysical conditions, phenological ecoregions useful for vegetation type and monitoring, potential areas susceptible to sudden oak death, an invasive oak pathogen, global aquatic ecoregions and susceptibility to aquatic invasives, and forest vertical structure ecoregions, using extensive LiDAR data sets. Multivariate Spatio-Temporal Clustering, which quantitatively places alternative future conditions on a common footing with present conditions, allows prediction of present and future shifts in tree species ranges, given alternative climatic change forecasts. Unsupervised statistical multivariate clustering of smoothed phenology data every 8 days over a 14-year period produces a detailed set of annual maps of national vegetation types, including major disturbances. Examining the constancy of these phenological classifications at a particular location from year to year produces a national map showing the persistence of vegetation, regardless of vegetation type. Using temporal unmixing methods, national maps of evergreen and deciduous vegetation can be produced. A by-cell regression trend line can be used to analyze decadal trends in forest health nationally. Forest Decline maps are a composite of insect, disease, and anthropogenic factors causing chronic decreases in the satellite greenness of these forests, including hemlock wooly adelgid, aspen decline, mountain pine beetle, wildfire, tree harvest, and urbanization. Because the trend in phenological changes monitors vegetation responses, all disturbance and recovery events are detected and mapped through the behavior of the vegetation itself. As ecological changes occur with increasing rapidity, these empirical data-mining approaches may be the quickest means to find the most-actionable ecological policies and directions.
Views: 91 FOSS4G NA
Data Quality Matters - Tech Vision 2018 Trend
 
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As more organizations push towards data driven decision making, integrity and quality of data become critical. See how a data intelligence practice can solve this.
Views: 14002 Accenture Technology
Targeted Marketing With Data Mining: Finding Consumers More Accurately Part 1
 
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Louise Keely is the Intellectual Capital Director at The Cambridge Group where she is involved in management consulting and developing growth strategies for clients that are driven by a superior understanding of profitable demand. In this video, she shares how they have been able to find consumers more accurately and actively using Salford Systems' data mining tools.
Views: 612 Salford Systems
What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning
 
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What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning - UNSTRUCTURED DATA definition - UNSTRUCTURED DATA explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents. In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially usable business information may originate in unstructured form. This rule of thumb is not based on primary or any quantitative research, but nonetheless is accepted by some. IDC and EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. The Computer World magazine states that unstructured information might account for more than 70%–80% of all data in organizations. The term is imprecise for several reasons: 1. Structure, while not formally defined, can still be implied. 2. Data with some form of structure may still be characterized as unstructured if its structure is not helpful for the processing task at hand. 3. Unstructured information might have some structure (semi-structured) or even be highly structured but in ways that are unanticipated or unannounced. Techniques such as data mining, natural language processing (NLP), and text analytics provide different methods to find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual tagging with metadata or part-of-speech tagging for further text mining-based structuring. The Unstructured Information Management Architecture (UIMA) standard provided a common framework for processing this information to extract meaning and create structured data about the information. Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication. Algorithms can infer this inherent structure from text, for instance, by examining word morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web page, or word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, …) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data". For example, an HTML web page is tagged, but HTML mark-up typically serves solely for rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page. XHTML tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms. Since unstructured data commonly occurs in electronic documents, the use of a content or document management system which can categorize entire documents is often preferred over data transfer and manipulation from within the documents. Document management thus provides the means to convey structure onto document collections. Search engines have become popular tools for indexing and searching through such data, especially text.....
Views: 796 The Audiopedia
Stream Data Mining: A Big Data Perspective
 
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Author: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas Abstract: Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 851 KDD2016 video

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