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s ls supportcountl supportcounts minconf l brPage 13br Step 5 brPage 14br Step 5 brPage 15br brPage 16br brPage 17br TID List of Items brPage 18br brPage 19br An FPTree that registers compresse d frequent pattern information brPage 20br brPage 21b. itemsets. : alternative representations and combinatorial problems. Too many frequent . itemsets. If {. a. 1. , . …. , a. 100. } . is a frequent . itemset. , then there are. . 1.27*10. 30 . frequent sub-patterns. LECTURE 4. Frequent . Itemsets. , Association Rules. Evaluation. Alternative Algorithms. RECAP. Mining Frequent . Itemsets. Itemset. A collection of one or more items. Example: {Milk, Bread, Diaper}. 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. . Special Topics in DBs. Large-Scale Data Management. Advanced Analytics . on Hadoop. Spring 2013. WPI, Mohamed Eltabakh. 1. Data Analytics. Include machine learning and data mining tools. Analyze/mine/summarize large datasets. and Algorithms. From . Introduction to Data Mining. By Tan. , Steinbach, Kumar. Association Rule Mining. Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction. Market Basket. Many-to-many relationship between different objects. The relationship is between items and baskets (transactions). Each basket contains some items (itemset) that is typically less than the total amount of items. itemsets. : alternative representations and combinatorial problems. Too many frequent . itemsets. If {. a. 1. , . …. , a. 100. } . is a frequent . itemset. , then there are. . 1.27*10. 30 . frequent sub-patterns. Bamshad Mobasher. DePaul . University. 2. Market Basket Analysis. Goal of MBA is to find associations (affinities) among groups of items occurring in a transactional database. has roots in analysis of point-of-sale data, as in supermarkets. Market Basket. Many-to-many relationship between different objects. The relationship is between items and baskets (transactions). Each basket contains some items (itemset) that is typically less than the total amount of items. 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.. 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. By. Shailaja K.P. Introduction. Imagine that you are a sales manager at . AllElectronics. , and you are talking to a customer who recently bought a PC and a digital camera from the store. . What should you recommend to her next? . : A Candidate Generation & Test Approach. Apriori. pruning principle. : If there is any . itemset. which is infrequent, its superset should not be generated/tested! (. Agrawal. & . Srikant.