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Current trends in Data Mining..
 
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Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II Msc cs A 175214141
Views: 575 vaishu raj
Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 5187 tutor2u
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 257322 Last moment tuitions
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: 412 BIG DATA
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: 49 aircc journal
What is TREND ANALYSIS? What does TREND ANALYSIS mean? TREND ANALYSIS meaning & explanation
 
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What is TREND ANALYSIS? What does TREND ANALYSIS mean? TREND ANALYSIS meaning - TREND ANALYSIS definition - TREND ANALYSIS 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 Trend analysis is the rampant practice of collecting information and attempting to spot a pattern. In some fields of study, the term "trend analysis" has more formally defined meanings. Although trend analysis is often used to predict future events, it could be used to estimate uncertain events in the past, such as how many ancient kings probably ruled between two dates, based on data such as the average years which other known kings reigned. In project management, trend analysis is a mathematical technique that uses historical results to predict future outcome. This is achieved by tracking variances in cost and schedule performance. In this context, it is a project management quality control tool. In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods, e.g. Mann-Kendall test, which is a version of Kendall rank correlation coefficient. For testing and visualization of non-linear trends also Smoothing can be used.
Views: 8147 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: 948 KDD2016 video
A Systematic Review on Educational Data Mining
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 75 Clickmyproject
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: 154 aircc journal
Big Data & Analytics for Finance
 
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Big Data & Analytics is a great opportunity for finance to bring more value to business. How companies can address this challenge? https://www.capgemini.com/consulting-fr/
Views: 14497 Capgemini
Twelve Emerging Trends in Data Analytics (part 1 of four)
 
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Original blog post: http://sctr7.com/2014/07/09/twelve-emerging-trends-in-data-analytics-part-1-of-4/ Business analytics is a practitioner movement uniting several disciplines to drive value-creating decisions from data. Central disciplines include IT / computer science, statistics, data management, decision science, and scientific research methods. Descriptive, predictive, and prescriptive approaches are often used to categorize particular methodological approaches, themselves derived from the fields of business intelligence, financial forecasting and econometrics, and operations management, respectively. While speculative in nature, the intention is to raise consciousness concerning long-term trends for the sake of practitioners, particularly for planners concerned with long-term strategy. 1. Plumbers wanted: data management overhead demands professional data mungers 2. Hardening models: increasingly complex models require tighter approaches to diagnostics and validation 3. The tunnel link: big data engineering and methodological approaches meet in the middle
Views: 361 sark7
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: 182 ijdkp jou
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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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: 4 aircc journal
Moore Methods - Text and Data Mining (2017 update)
 
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Researchers often have to go through lots of articles and papers to find key information for their own work. This can take quite a long time but what if there was a method that could help? In this video, we give an overview of Text and Data Mining (TDM). TDM is an interesting technique that can help with analysing text and other information quickly, allowing you to get results and get on with your work. Want to take things further? Check out our blog for more learning opportunities and activities: https://23researchthingscam.wordpress.com/2016/11/23/thing-19-text-and-data-mining/
Views: 278 Moore Library
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. 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: 35 aircc journal
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: 695 Ria Liuswani
TCT SAS Data Mining and Risk Management Technology and Job Market Trends(02).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 brfore 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: 340 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. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 52 aircc journal
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: 21 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 [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: 5 aircc journal
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: 23 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 [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: 14 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, 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 )
 
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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: 21 aircc journal
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: 160 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 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: 30 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: 3447 tutor2u
Scitics Visual Trend Analytics: Detecting Trends for Technology Management based on Library Data
 
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Who recognizes the signs of the times has an advantage on the market. The earlier the identification of upcoming topics and trends is possible the better. The research Group Human-Computer Interaction and Visual Analytics (h_da | vis) has developed a solution with Scitics to identify and assess market trends at an early stage. Companies need to identify trends early and deal specifically with market and technological developments in order to respond to increasing competitive pressure and rapid changes in their industry can. The Scitics Visual Trend Analysis solutions is a novel technical approach to analyze the developments on the market with graphical and explorative visualizations. Via usage and combining Visual Analytics, Data Mining and Business Analytics approaches a differently thought analysis solution could now be introduced with the goal of detecting and overserving technology trends, creators/inventors and competitors. This enables a highly efficient and even more effective analysis to consider market trends at an early stage. More information about our Visual Trend Analytics solution you will find under: http://s.vis.h-da.de/scitics. More about our team and further research action you will find under: http://vis.h-da.de.
Views: 21 HDA VIS
Document Management Trends
 
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Dan Wajzner explains in this video how businesses rely on information, from simple records to the use of data mining tools, into which document management (DM) introduces efficiencies in any industry sector. DM today comprises logically grouped data as well as historical paper documents, which means DM is often essential infrastructure, rather than a standalone application, that is capable of providing a BI dashboard of opportunities and threats.
Views: 228 Document Logistix
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: 94 Sivakumar Arumugam
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.
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 19221 MATLAB
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: 17 Ijaia Journal
Time Series: Measurement of Trend in Hindi under E-Learning Program
 
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It covers in detail various methods of measuring trend like Moving Averags & Least Square. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce & Management
Introduction to Regression Analysis
 
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This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.
Views: 189616 Mathispower4u
"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. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer 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: 1999 Quantopian
Interpreting the Consumer Story: Text Analytics in Market Research
 
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Do you have customer service on your mind? Are you unsure how to analyze your customers' written comments? Text data exist all over the internet. Emails, forum questions, social media posts, customer service requests, answers to open-ended survey questions, and even this blog post are all sources of raw text data. Text data are drawn from anything written by a human being, and these raw text data can be analyzed to reveal insightful trends. We’ve got analytic techniques that will benefit your business! Learn more here: http://murphyresearch.com/text-analytics-in-market-research ---- Murphy Research Named Again One of Best Places to Work in LA http://www.murphyresearch.com/murphy-research-named-one-best-places-work-la 🎉🎉🎉 Murphy Research's Beautiful New Office Space http://murphyresearch.com/murphy-research-beautiful-new-office-space/ Why Working for Murphy Research Rocks! http://murphyresearch.com/why-working-for-murphy-research-rocks Subscribe to our Murphy Research channels here: http://www.murphyresearch.com https://www.facebook.com/MurphyResearch https://twitter.com/MurphyResearch https://www.linkedin.com/company/murphy-research https://plus.google.com/u/0/+Murphyresearch/posts http://www.instagram.com/MurphyResearch
Views: 356 Murphy Research
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: 34 Finextra Research
Simplify Clinical Trials Performance Management | Cognizant® SmartTrials™
 
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For any biopharmaceutical company, finding ways to reduce the time to introduce a new drug is enormously valuable – if only to recognize revenue earlier. These companies are beginning to discover the huge opportunity that data and analytics offer in accelerating decision-making – in order to close a study sooner. Cognizant® SmartTrials™ is the premier solution for clinical trials performance management. It aggregates, organizes, and visualizes clinical trial information — and turns data into decisions. Learn more: http://cogniz.at/2nuQx8X Subscribe to this channel: http://cogniz.at/subscribeyt #digitaltransformation
Views: 493 Cognizant
[LAK 2012] May 1: 7 - Learning Analytics and Educational Data Mining
 
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George Siemens Ryan S. J. d. Baker Learning Analytics and Educational Data Mining: Towards Communication and Collaboration
Planning Tools: Google Trends
 
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Boost your marketing with this powerful planning tool. For more Google insights and trends visit thinkwithgoogle.co.uk
Views: 24249 Think with Google UK
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: 2540 HasGeek TV
The Internet of Things, Big Data and Privacy: Future Directions for Marketing Research
 
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Professor Robert W Palmatier, the John C. Narver Endowed Professor in Business and Administration in the Marketing Department at the University of Washington’s Foster School of Business, visited the Indian School of Business in April 2018 as part of the Thought Leaders Conference on Managing Business and Innovation in Emerging Markets. Professor S Arunachalam met him to discuss emerging trends in marketing and research.
William Paiva: Transforming health care and medical education through clinical Big Data analytics
 
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Health care is undergoing significant transformation, and digital health data is at the center of this change. According to the Centers for Disease Control, nearly 80 percent of the nation’s health care institutions have converted to an electronic medical record (EMR) system from the old paper-based system. New technologies like smartphone applications are also creating new stockpiles of digital data. Genetic data is growing as well; scientists can sequence a person’s entire DNA within 24 hours and for less than $1,000. Collectively, the amount of digital health data is expected to grow from 500,000 to 25 million terabytes over the next five years. Why do we care that our health information is now in a digital format? How does it benefit all of us? People who work in health care—and every industry for that matter—are smart, well trained, and do their best to stay up-to-date with the latest research, methodologies and trends. However, it is not rational to assume individuals have the depth of knowledge or data access to deal with every situation they encounter. Furthermore, the health care field is already understaffed, and this issue will only get worse as the looming mass retirement of baby boomers from the health care workforce creates an unprecedented supply-and-demand crisis. Digitized health data has the potential to help mitigate this troubling situation. Predictive medicine uses computing power and statistical methods to analyze EMR and other health-related data to predict clinical outcomes for individual patients. Beyond health outcome forecasting, predictive medicine also can uncover surprising and often unanticipated clinical associations. Oklahoma State University’s Center for Health Systems Innovation (CHSI), through its Institute for Predictive Medicine (IPM), is a leader in the exploding field of predictive medicine thanks to the unprecedented donation by Cerner Corporation of its HIPAA-compliant clinical health database, one of the largest available in the United States. Specifically, this dataset represents clinical information from over 63 million patients and includes admission, discharge, clinical events, pharmacy, and laboratory data spanning more than 16 years. Over 20 full-time CHSI employees and nearly two dozen graduate students are working to execute the CHSI mission to transform rural and Native American health through data analytics. Further, CHSI has a number of ongoing partnerships with academia, health systems and corporations to extract value from digitized health data. One example of CHSI’s numerous predictive medicine projects is an effort to help physicians determine whether the performance of particular cardiovascular drugs varies by gender or race, or both. Conversely, this study will help indicate which drugs perform poorly or even cause complications in these populations. Other CHSI studies are designed to give physicians insight into whether patients with a particular disease are likely to develop or already have an associated disease, which will aid in co-managing these conditions and lead to better health care. Another project is designed to help hospitals use data on patient demographic characteristics, comorbidities, discharge setting, and other medical information contained in comprehensive EMR systems to determine if patients are at high risk for being readmitted for disease-associated complications. If patients are considered high risk, they can get the care and support necessary to prevent frequent cycling through the health care system. Predictive medicine can also lead to the creation and implementation of tools for managing larger patient loads, which can aid health care providers in dealing with supply-and-demand problems. For instance, CHSI has developed a clinical decision support system that can detect diabetic retinopathy with a high degree of accuracy using lab and comorbidity data available through primary care visits. This algorithm addresses the very real challenge of low patient compliance, particularly among rural and underserved populations, with annual ophthalmic eye exams, which are the gold standard for retinopathy detection and preventing vision impairment or total vision loss. CHSI is extending this work to other common diabetes-related microvascular complications with the goal of developing a comprehensive suite of tools that can help increase prevention and management of these complications among the nation’s growing diabetic population.
Views: 1245 Stanford Medicine X
Data Mining E-Learning Www.CreateSmiths.Com
 
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Data Mining E-Learning at Www.CreateSmiths.Com by Blair Smith
Views: 262 site3e
12   Demonstration of Data Mining Tools KNIME
 
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www.soofastaei.net
Views: 89 Ali Soofastaei
28c3: Data Mining the Israeli Census
 
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Download high quality version: http://bit.ly/vDnQu4 Description: http://events.ccc.de/congress/2011/Fahrplan/events/4652.en.html Yuval Adam: Data Mining the Israeli Census Insights into a publicly available registry The entire Israeli civil registry database has been leaked to the internet several times over the past decade. In this talk, we examine interesting data that can be mined and extracted from such database. Additionally, we will review the implications of such data being publicly available in light of the upcoming biometric database. The Israeli census database has been freely available on the Internet since 2001. The database has been illegally leaked due to incompetent data security policies in the Ministry of Interior of Israel, which is responsible for the management of the Israeli census. The data available includes all personal data of every Israeli citizen: name, ID number, date and location of birth, address, phone number and marital status, as well as linkage to parents and spouses. In this talk we discuss various statistics, trends and anomalies that such data provides us with insight to. Personal details will obviously be left out of the talk, though it is important to note that any person who wishes to retrieve such details can easily do so. We will end the talk with a discussion about upcoming and relevant privacy issues in light of Israel's soon-to-be biometric database.
Views: 2875 28c3
Advanced Keyword SEO Management Tool: Web-based Software for Cloud-based Data Mining
 
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Back Azimuth http://www.back-azimuth.com now offers its VOCDMS (Voice of the Consumer Data Management System) as part of its advanced keyword management services. Back Azimuth's VOCDMS is a cloud-based data-mining platform that aggregates keyword and social media data into a single web-based application enabling marketers to better understand their prospects while identifying performance gaps that are overlooked by other tools. Search Marketers have dreamed of a single database for managing all of their keyword data -- paid, organic and site search. It is now possible. Using the scale of the cloud and Back Azimuth's VOCDMS database solution, we are able to combine the data and then mine it in hundreds of different ways to help marketers find opportunities which were never possible before. In this video, Bill Hunt, founder and president, Back Azimuth, discusses his company's consulting for a huge online retailer, Yummie Tummie. 0:09 Bill Hunt, founder and president, Back Azimuth Consulting, discusses the ideal target customer who has more than a million keywords that can benefit from Back Azimuth advanced keyword management software or VOCD MS. These are large travel, computer, etail, and retail companies that get a large number of keyword queries that would bring people to their products. 0:22 Bill Hunt speaks to a group of digital marketers at SES. Bill asks the audience about their client-base and how many keywords they monitor and where the opportunities that are not being taken advantage of are. 0:47 Bill expands on the challenges faced by companies that have a million or more keywords and how there are hundreds of thousands of ways in which consumers will look for their product and each of these keywords has a different value. Some of the keyword queries will convert, some won't. The best thing VOCDMS can do for companies is to help them understand how to find those keywords that will convert and generate the most value for them. 01:11 Yummie Tummie (www.yummielife.com/) case study -- Bill begins by presenting a company product which could be referenced using different descriptions. These product names included girdle, shapewear, shaping underwear, shapewear intimates, and Spanx. The age differential is significant, according to Bill, because anyone over the age of 30 would describe the product as a girdle and anyone under the age of 30 would call it spanx or shapewear. The brand, in this case, wants to call it, Shapewear Intimate. Bill describes how the VOCDMS keyword management software discovered that 75% of the entire search related to shapewear came down to a simple formula: clothing type + shapewear. Bill says you have to match the interest and intent to what your prospective customer wants. 04:15 Bill describes how his company revamped Yummie Tummie's keyword research by rebuilding the company's entire website creating a whole new site taxonomy which translates into an experience that provides the user with progressive levels of information about shapewear. 07:14 Bill discusses how the VOCDMS solution mines big keyword data, by managing it in a single database with filters that look for the needle in a haystack. VOCDMS is used by large travel companies, electronic manufacturers, similar to an HP or Dell, that have large sets of data. Travelocity is a client that has tons of keyword volume from different destinations. It takes advantage of VOCDMS by targeting their product to the sets of keywords that their customer base uses for searching. 08:37 To learn more about how your company can benefit from advanced keyword management and VOCDMS, please visit: http://back-azimuth.com/contact Phone: 866-599-8225 If you are interested in a pilot project and want to see how Back Azimuth Consulting's Voice of the Consumer Data Management System can work for you, all that is required is access to your main data such as web analytics, paid search data, and any kind of segmentation or keyword work that you have already done previously.
Views: 806 Bill Hunt
Leveraging Google Data Tools for Social Science Studies (Qing Wu)
 
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DataEDGE Conference 2017 — UC Berkeley School of Information http://dataedge.ischool.berkeley.edu/2017/ Google provides to the public a whole range of data sources (Google trends, Google correlate, shopping insights), analytic tools (Google analytics, tensor flow, and external R packages), and survey tool (google consumer survey). In this talk I will showcase how we can use them to build models to explore insights and causal relationship in various social science topics in many different fields. . . . . . . . . . . . . . . . . . . Dr. Qing Wu Senior Economist Google Qing Wu works on business intelligence, quantitative analysis in on-line advertising, revenue forecast and management, user/advertiser behavior modeling, and macroeconomics for Google. His specialties include internet data mining, financial forecast, macroeconomics, econometrics, supply chain and demand chain management.