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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: 226899 Last moment tuitions
Intelligent Heart Disease Prediction System using Data Mining Techniques
 
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Intelligent Heart Disease Prediction System using Data Mining Techniques To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
Views: 198 jpinfotechprojects
DSS - Decision Support System
 
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Decision Support System
Views: 6872 asvd2010
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 33573 GRIETCSEPROJECTS
ALT Data Mining Software - Demo Video
 
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This VIDEO demonstrates the use of the DATA MINING function in the Automatic Lead Tools software. The data extraction is highly accurate; it's cloud-based; offers unlimited Lead Extractions; uses Search Engines and Directories; Auto-Catalogs within the system, and can easily print mailing labels for Direct Mail.
An Example Application of Data Mining
 
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Have a look at one of our decision support systems powered by our data mining algorithms.
AssetWise - Converge operational and IT data for decision support
 
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Combining IT with operational technology is no longer a barrier with the ability to merge data from the back room with data from the operational floor. Learn more at: https://www.bentley.com/en/products/product-line/operational-analytics-software/assetwise-amulet
Mining Patterns in Data using Search Algorithms
 
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Large amounts of data are nowadays available in many areas of industry and science. Prof. Siegfried Nijssen argues that many problems concerning the analysis of data can be seen as constraint-based data mining problems and discusses the efficient algorithms that he developed to solve these problems.
Decision Tree Important Points ll Machine Learning ll DMW ll Data Analytics ll Explained in Hindi
 
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Decision Tree Explained with Example https://youtu.be/RVuy1ezN_qA 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 25174 5 Minutes Engineering
Data Wrangling with DSS: From Scraping HTML To Unsupervised Learning in 1h
 
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During this talk, Henri will demonstrate how we can use Data Science Studio (DSS) to create a complete workflow from raw data to training models in 1h. We will start by scraping data science related job listings in New York. Then, we will download all of the company reviews and try to make sense of where is the best place to work by cleaning and parsing raw html, and ultimately performing unsupervised learning to see what topics come up! Finally, we will use DSS's insight tool to create a web app using flask, html and javascript to explore the results. Our Speaker: Henri Dwyer is a data scientist and engineer working at Dataiku on building the best platform for data scientists. He received an MSc in Engineering from Columbia University in New York City, and a BS and an Ms in Engineering from Ecole Polytechnique in Paris. He now lives in Brooklyn, and is always keen on discovering new data science problems to solve.
Analytica K-12 Decision Support System from EIS Education
 
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Analytica is an advanced decision support system for K-12 educators. Hosted on a district's own private Microsoft Azure cloud, it brings together data and content from disparate school systems into a central web based suite for use by teachers, campus leaders and district administrators. Visit www.EisEducation.com to learn more.
Views: 279 EIS INC
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1696 Audiopedia
North American Knowledge-based/Evidence-based Clinical Decision Support System (CDSS) market
 
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The knowledge-based/evidence-based CDSS market in North America is expected to reach around $283.3 million by 2018, at a developing CAGR of 8.1% from 2013 to 2018. http://www.micromarketmonitor.com/market/north-america-knowledge-based-evidence-based-cdss-7576570691.html
Views: 89 Leandro Tandy
Cloud-Based Clinical Decision Support Solutions | FDB & MEDITECH
 
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www.fdbhealth.com/fdb-medknowledge What if two of the leading healthcare IT companies joined together to reimagine how drug knowledge is delivered to clinicians? First Databank (FDB) and MEDITECH have collaborated to develop the FDB Cloud Connector, which will enable FDB customers to access drug knowledge through Web Services in the cloud. This industry-changing collaboration from two of healthcare IT’s leading companies will fundamentally change how drug knowledge is accessed, used, and delivered. FDB solutions are engineered for interoperability with most healthcare information systems, so implementation is seamless. We believe technology solutions should work for clinical and administrative health professionals, rather than vice versa. We welcome partnerships that accelerate our mission to provide configurable and targeted data to guide clinical decision making. Click the link above to see how FDB is promoting innovation in healthcare through strategic partnerships for better drug knowledge.
Views: 917 FDBFirstDatabank
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 218457 Google Developers
Critical study on Data Mining with IDSS using RAPID technique for Diabetes
 
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Critical study on Data Mining with IDSS using RAPID technique for Diabetes To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: http://www.jpinfotech.org In data mining, knowledge is extracted using key elements and concepts after identifying relevant and reliable data. But in the field of health care, researchers are finding it difficult to convert the bio-medical database into knowledge at a rapid pace. The medical data is huge, complex and heterogeneous in nature. Data Mining principles& tools are used in conjunction with health care expert systems to extract inherent relationships among data elements as knowledge. By integrating different data mining concepts with expert systems, a new system called “Integrated Decision Support System” (IDSS) is proposed, which can provide better results compared to existing ones. It converts knowledge into useful format and uses different tools for construction of its architecture. To reduce possible solutions for diabetic diagnosis, Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Web Based Portal Joint Asia Diabetes Evaluation( JADE) programs are integrated with Reliable Access and Probabilistic Inference based on clinical Data (RAPID) in the developed IDSS system to enhance existing systems for fast extraction of knowledge.
Views: 31 jpinfotechprojects
Intelligent Heart Disease Prediction System Using Data Mining Techniques || in Bangalore
 
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The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 34969 Red Apple Tutorials
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. 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 areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases 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/ijdms/index.html
Views: 7 Ijdms Journal
OLAP vs OLTP in hindi
 
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#olap #oltp #datawarehouse #datamining #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: 115183 Last moment tuitions
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 464657 Brandon Weinberg
TN TRB Computer Science Syllabus - Business Computing #5 Data Mining
 
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Tamil Nadu TRB Computer Science Instructor GRADE1 Exam Syllabus Business Computing - Data Mining ------ Data Mining • It's the process to find patterns or relationship of Data using algorithms. • It's a process of analysing data from different perspectives and summarising it into useful information. • It gives answer which Data Base cannot give Data • Raw fact Eg: • Petrol price is Rs.75 per litter on 1st April • Petrol price is Rs.76.25 PL on 2nd April This data (price and date) stored in DB Information • We can get some information from Data Eg: • Price of the petrol is between Rs70 to Rs80 per litter in April Knowledge • From the useful Data and useful Information we can get some knowledge Eg: • When Petrol price is increasing by Rs5, the inflation rating is increased by 2% Data Mining: Extract the useful Decision / Answer from the Data Data • You can trust this Data, which is always correct based on current status that is stored in DB Information • The information gathered from Data is dynamic, which is getting changed based on Data • May be different for different time / place / person • Information is collected from Data. Data Mining Process / Life cycle 1. Data • Raw data from DB 2. Target Data • Split the necessary data • Selection of Data 3. Pre-processed Data • To remove unnecessary data • Deduct missing data 4. Transformed Data • Save the data in different form which can be mined • Normalized the data 5. Mining the Data • Extract the decisions by Patterns / Templates • By mathematical rules and algorithms • Also called machine-learning algorithms 6. Knowledge • Interpret the patterns to knowledge by user • From the Mined data template / pattern we can get the knowledge Extracting the knowledge from the Data is Data Mining EG of Machine-Learning algorithms • Classification learning • Numeric estimation • Association learning • and more Classification learning • Classify the characteristics of Objects/Entities • EG: A consumer will buy a new car in next year = Yes / No • Train the Machine using training data from the data what we have (Transformed data and Patterns) Appling the decision tree, showroom member can predict the new customer ability.
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #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: 75845 Last moment tuitions
Web Based Medical Decision Support Systems For hree Way Medical Decision Making With Game Theoretic
 
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SPIRO GROUP OF COMPANIES For ECE,EEE,E&I, E&C & Mechanical,Civil, Bio-Medical #1, C.V.R Complex, Singaravelu St, T.Nagar, Chennai - 17, (Behind BIG BAZAAR)Tamilnadu,India Mobile : +91-9962 067 067, +91-9176 499 499 Landline : 044-4264 1213 Email: [email protected] For IT, CSE, MSC, MCA, BSC(CS)B.COM(cs) #78, 3rd Floor, Usman Road, T.Nagar, Chennai-17. (Upstair Hotel Saravana Bhavan) Tamilnadu,India Mobile: +91-9791 044 044, +91-9176 644 044 E-Mail: [email protected]
Views: 59 spiroprojects
MontageDSS English
 
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Web-based feed formulation and decision support system for ruminant by MARDI Malaysia
Views: 17 Nasyatul Ekma
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. 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 areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases 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 : November 18, 2017 Acceptance Notification : December 18, 2017 Final Manuscript Due : December 26, 2017 Publication Date: Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdms/index.html
Views: 4 Ijics Journal
IJDMS
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. 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 areas of Database management systems. Topics of interest include, but are not limited to, the following • • Constraint Modelling and Processing • Data and Information Integration & Modelling • Data and Information Networks • Data and Information Privacy and Security • Data and Information Quality • Data and Information Semantics • Data and Information Streams • Data Management in Grid and P2P Systems • Data Mining Algorithms • Data Mining Systems, Data Warehousing, OLAP • Data Structures and Data Management Algorithms • Database and Information System Architecture and Performance • DB Systems & Applications • Digital Libraries • Distributed, Parallel, P2P, and Grid-based Databases • Electronic Commerce and Web Technologies • Electronic Government & eParticipation • Expert Systems and Decision Support Systems • Expert Systems, Decision Support Systems & applications • Information Retrieval and Database Systems • Information Systems • Interoperability • Knowledge Acquisition, discovery & Management • Knowledge and information processing • Knowledge Modelling • Knowledge Processing • Metadata Management • Mobile Data and Information • Multi-databases and Database Federation • Multimedia, Object, Object Relational, and Deductive Databases • Pervasive Data and Information • Process Modelling • Process Support and Automation • Query Processing and Optimization • Semantic Web and Ontologies • Sensor Data Management • Statistical and Scientific Databases • Temporal, Spatial, and High Dimensional Databases • Trust, Privacy & Security in Digital Business • User Interfaces to Databases and Information Systems • Very Large Data Bases • Workflow Management and Databases • WWW and Databases • XML and Databases 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/ijdms/index.html
Views: 11 Ijdms Journal
ijdms
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. 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 areas of Database management systems. Topics of interest include, but are not limited to, the following • Constraint Modelling and Processing • Data and Information Integration & Modelling • Data and Information Networks • Data and Information Privacy and Security • Data and Information Quality • Data and Information Semantics • Data and Information Streams • Data Management in Grid and P2P Systems • Data Mining Algorithms • Data Mining Systems, Data Warehousing, OLAP • Data Structures and Data Management Algorithms • Database and Information System Architecture and Performance • DB Systems & Applications • Digital Libraries • Distributed, Parallel, P2P, and Grid-based Databases • Electronic Commerce and Web Technologies • Electronic Government & eParticipation • Expert Systems and Decision Support Systems • Expert Systems, Decision Support Systems & applications • Information Retrieval and Database Systems • Information Systems • Interoperability • Knowledge Acquisition, discovery & Management • Knowledge and information processing • Knowledge Modelling • Knowledge Processing • Metadata Management • Mobile Data and Information • Multi-databases and Database Federation • Multimedia, Object, Object Relational, and Deductive Databases • Pervasive Data and Information • Process Modelling • Process Support and Automation • Query Processing and Optimization • Semantic Web and Ontologies • Sensor Data Management • Statistical and Scientific Databases • Temporal, Spatial, and High Dimensional Databases • Trust, Privacy & Security in Digital Business • User Interfaces to Databases and Information Systems • Very Large Data Bases • Workflow Management and Databases • WWW and Databases • XML and Databases 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/ijdms/index.html
Views: 10 Ijdms Journal
iSERM  - Intelligent Environmental Decision Support System
 
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Wildfire risk assessment via statistical measurements on environmental data and forecasting by neural network in deep learning architecture The code can be found on github : https://github.com/PanosNikolaou/iSERM
Views: 150 Panagiotis Nikolaou
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Using Data Mining to Predict Hospital Admissions From the Emergency Department
 
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Using Data Mining to Predict Hospital Admissions From the Emergency Department -- The World Health Organization estimates that by 2030 there will be approximately 350 million young people (below 30 to 40 years) with various diseases associated with renal complications, stroke and peripheral vascular disease. Our aim is to analyze the risk factors and system conditions to detect disease early with prediction strategies. By using the effective methods to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This system highlights the potential utility of three common machine learning algorithms in predicting patient admissions. In this proposed approach, we considered a heart disease as a main concern and we start prediction over that disease. Because in India a strategic survey on 2015-6016 resulting that every year half-a million of people suffer from various heart diseases. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM's will be useful where accuracy is paramount. Using the strategic algorithm such as Logistic Regression, Decision Trees and Gradient Boosted Machine, we can easily identify the disease with various attributes and risk factor specifications. Based on these parameters, the analysis of high risk factors of developing disease is identified using mining principles. Use of data mining algorithms will result in quick prediction of disease with high accuracy. Data mining, emergency department, hospitals, machine learning, predictive models -- For More Details, Contact Us -- Arihant Techno Solutions www.arihants.com E-Mail-ID: [email protected] Mobile: +91-75984 92789
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 138164 Brandon Weinberg
Business Analytics Office Hour: Career Oriented: September 5, 2017
 
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Chris Martin interviews Tim Walters about his career and advice for budding analysts. Tim Walters, President of InfoTech Marketing, possesses 35 years of marketing, finance, planning, and operations experience. Since founding InfoTech Marketing in 1995, he has assisted a wide array of firms with marketing strategy development, marketing research, competitive intelligence, data mining, customer relationship management, financial planning, pricing, logistics, data warehousing, and decision support systems. In managing client projects, Tim has successfully worked with diverse individuals from around the world. His knowledge of various management tools and techniques allows each project to be customized to the client’s needs and capabilities. Tim also serves as an adjunct professor of technology management at the University of Denver, so he must keep up with the latest technologies. From Web-based research to data mining, Tim applies the latest effective methodologies to help clients with their problems. Prior to founding InfoTech Marketing, Tim launched new high tech products and had profit and loss responsibility for the general business market segment at US WEST's enhanced services business. He set new revenue growth records (up 100%), established a new corporate direct mail response rate standard, and implemented data warehousing and sophisticated forecasting models for senior management. Tim began his marketing career with a leading transportation company. He rapidly rose to the top marketing position, where he served as Director of Marketing, Finance and Planning. He led the company to achieving a 30% growth rate while the market was stagnant. He introduced new technology marketing concepts to the company and the industry. Improving the company’s bottom-line was his primary focus, and Tim analyzed various acquisition candidates and participated in the divestiture of one subsidiary. Tim also oversaw the operations of a profitable subsidiary in a related industry. In addition to his excellent experience, Tim also obtained a M.B.A. degree in Strategic Planning Systems from the prestigious Wharton School, where he secured admission to the leading honor fraternity. He also graduated magna cum laude with a B.S. in Public Administration from the University of Missouri.
Views: 95 Springboard
International Journal of Service Science, Management, Engineering, and Technology
 
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International Journal of Service Science, Management, Engineering, and Technology Ahmad Taher Azar (Benha University, Egypt) and Ghazy Assassa (Benha University, Egypt) http://www.bu.edu.eg/staff/ahmadazar14 Now Available Year Established: 2010 Publish Frequency: Quarterly ISSN: 1947-959X EISSN: 1947-9603 https://www.igi-global.com/journal/international-journal-service-science-management/1132 ___________ Description: The International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is a multidisciplinary journal that publishes high-quality and significant research in all fields of computer science, information technology, software engineering, soft computing, computational intelligence, operations research, management science, marketing, applied mathematics, statistics, policy analysis, economics, natural sciences, medicine, and psychology, among others. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques. ___________ Topics Covered: • Agent technologies • Agricultural applications • Agricultural traceability and food safety • Artificial Intelligence • Autonomous systems • Big data technologies and management • Biochemistry • Biomedicine and bioinformatics • Biotechnology • Business Information Systems • Clinical decision support • Cloud Computing • Computational Intelligence • Computational techniques for service operations • Data mining and data security • Decision theory • Disease detection, management and monitoring • Distributed intelligence • Drug discovery • Ecological system modeling • Economic aspects of the service sector • Embedded sensor and mobile database • Evolutionary computing • Expert Systems • Financial innovation • Financial statements analysis • Fraud management • Geographic Information Systems • Heuristics • Image Processing • Information Technology • Intelligent systems and data mining • Life science and medical research • Machine Learning • Management accounting • Markov chains • Models of service systems, services as complex systems • Network management contingency issues • Neuroscience • Optimization Techniques • Pharmaceutical science • Policy, privacy, security, and legal issues regarding services • Reasoning and inferences • Security in software architecture and design • Security patterns • Sensor design, sensor-fusion and sensor-based control • Service design and modeling • Service innovation and marketing • Service oriented architecture and technologies • Service performance measurement and analysis • Service quality measurement, benchmarking, and management • Service risk management • Social Networking • Soft Computing • Software engineering • Stochastic models • Strategic Planning • Supply Chain Management • Systems engineering • Telecommunications and networking technologies • Teleoperation and telerobotics • Venture capital • Virtual Reality • Web informatics • Web intelligence and mining • Web services and technologies • Working capital management
Views: 84 IGI Global
International Journal of Database Management Systems ( IJDMS )
 
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International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. 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 areas of Database management systems. Topics of interest include, but are not limited to, the following • Constraint Modelling and Processing • Data and Information Integration & Modelling • Data and Information Networks • Data and Information Privacy and Security • Data and Information Quality • Data and Information Semantics • Data and Information Streams • Data Management in Grid and P2P Systems • Data Mining Algorithms • Data Mining Systems, Data Warehousing, OLAP • Data Structures and Data Management Algorithms • Database and Information System Architecture and Performance • DB Systems & Applications • Digital Libraries • Distributed, Parallel, P2P, and Grid-based Databases • Electronic Commerce and Web Technologies • Electronic Government & eParticipation • Expert Systems and Decision Support Systems • Expert Systems, Decision Support Systems & applications • Information Retrieval and Database Systems • Information Systems • Interoperability • Knowledge Acquisition, discovery & Management • Knowledge and information processing • Knowledge Modelling • Knowledge Processing • Metadata Management • Mobile Data and Information • Multi-databases and Database Federation • Multimedia, Object, Object Relational, and Deductive Databases • Pervasive Data and Information • Process Modelling • Process Support and Automation • Query Processing and Optimization • Semantic Web and Ontologies • Sensor Data Management • Statistical and Scientific Databases • Temporal, Spatial, and High Dimensional Databases • Trust, Privacy & Security in Digital Business • User Interfaces to Databases and Information Systems • Very Large Data Bases • Workflow Management and Databases • WWW and Databases • XML and Databases 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/ijdms/index.html
Views: 115 Ijdms Journal
Clinical Decision Support (CDS)
 
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How to view and use CDS alerts.
Views: 1601 BusinetLLC
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
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** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ** This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial: 1. What Is The Need For BI? 2. What Is Data Warehousing? 3. Key Terminologies Related To DWH Architecture: a. OLTP Vs OLAP b. ETL c. Data Mart d. Metadata 4. DWH Architecture 5. Demo: Creating A DWH - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Intelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 253757 edureka!
Webinar | Solar for Irrigation: Using a Decision Support Tool to Guide Action | 6 June 2018
 
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Webinar on 'Solar for Irrigation: Using a Decision Support Tool to Guide Action', organised in association with Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) and with support from German Federal Ministry of Economic Cooperation and Development. The Council introduced a web-based, data-driven decision support tool for India, developed leveraging its research on solar for irrigation over the past two years. The tool currently focuses on India, but can be expanded/replicated to suit the needs of stakeholders in other geographies across the globe, particularly the Global South. The tool assists policymakers, financiers, entrepreneurs, and others in planning and decision-making for the deployment of solar-based irrigation. It helps stakeholders to prioritise regions and identify suitable deployment strategies for solar for irrigation, based on location specific context. The tool provides detailed insights for each district of India. CEEW's Solar Pumps Tool: https://bit.ly/2xaTAVw
Views: 30 CEEWIndia
L1: Data Warehousing and Data Mining |Introduction to Warehousing| What is Mining| Tutorial in Hindi
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L1: Data Warehousing and Data Mining | What is Warehousing| What is Mining| Tutorial in Hindi Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 1332 University Academy
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: 4497 tutor2u
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 103148 Siraj Raval
What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning
 
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What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning - CASE-BASED REASONING definition - CASE-BASED REASONING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deeply explored in cognitive science. Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: 1. Retrieve: Given a target problem, retrieve from memory cases relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.
Views: 5265 The Audiopedia
FP Tree Algorithm For Construction Of FP Tree Explained with Solved Example in Hindi (Data Mining)
 
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FP Tree Construction Correction video https://youtu.be/8eAorA2lhYc 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5-MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5-MINUTES ENGINEERING 📚📚📚📚📚📚📚📚 #FPTree #ConstructionOfFPTree #DataMining
Views: 14191 5 Minutes Engineering

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