PPT-Association Rule Mining Debapriyo Majumdar

Data Mining Fall 2014 Indian Statistical Institute Kolkata August 4 and 7 2014 Transaction id Items 1 Bread Ham Juice Cheese Salami Lettuce 2 Rice Dal Coconut Curry leaves Coffee Milk Pickle. Prepared by : . Ajit. . Padukone. ,. . . Komal. . Kapoor. Outline. Association Rule Mining. Applications. Temporal Association Rule Mining. Existing Techniques and their Limitations. November 5. th. , 2013. Parallel Association Rule Mining. Outline. Background of Association Rule Mining. Apriori Algorithm. Parallel Association Rule Mining. Count Distribution. Data Distribution. Candidate Distribution. Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Data Mining and Knowledge Discovery . Prof. Carolina Ruiz and Weiyang Lin. Department of Computer Science. Worcester Polytechnic Institute. Sample Applications. Sample Commercial Applications. Market basket analysis. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 4 and 7, 2014. Transaction id. Items. 1. Bread, Ham, Juice,. Cheese, Salami, Lettuce. 2. Rice, . Dal, Coconut, Curry leaves, Coffee, Milk, Pickle. Risk Prediction. Gyorgy J. Simon. Dept. of Health Sciences Research. Mayo Clinic. SHARPn. Summit 2012. Outline. Introduction. Modeling Diabetes Risk. Association Rule Mining. Results. Diabetes Disease Network Reconstruction. 12-. 1. Data mining is a rapidly growing field of business analytics focused on better understanding of characteristics and patterns among variables in large data sets.. It is used to identify and understand hidden patterns that large data sets may contain.. What Is Association Rule Mining?. Association rule mining. . is finding frequent patterns or associations among sets of items or objects, usually amongst transactional data. Applications include Market Basket analysis, cross-marketing, catalog design, etc.. Lecture Organization (Chapter 7). Coping with Categorical and Continuous . Attributes . shortened version in 2015. Multi-Level Association Rules . skipped in . 2015. Sequence Mining . © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 . Core Methods in Educational Data Mining EDUC 691 Spring 2019 Assignment BA4 Questions? Comments? Concerns? Association Rule Mining Today’s Class The Land of Inconsistent Terminology Association Rule Mining Presented by: Ramoza Ahsan and Xiao Qin November 5 th , 2013 Parallel Association Rule Mining Outline Background of Association Rule Mining Apriori Algorithm Parallel Association Rule Mining Count Distribution Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November . 20, . 2014. Examples of Streaming Data. Ocean behavior at a point. Temperature (once every half an hour). Surface height (once or more / second). Global . and Local Association Rules. Abhishek Mukherji*. . Elke . A. . . Rundensteiner Matthew . O. . Ward. Department of Computer Science, Worcester Polytechnic Institute, MA, USA. Information Retrieval – Spring 2015. Indian Statistical Institute Kolkata. Search engines. 2. User needs some information. Assumption: the required information is present somewhere. A search engine tries to bridge this gap.