Transactional data Algorithm Applications Association Rules Transactional data Algorithm Applications 11/26/2018 CSE591: Data Mining by H. Liu

Market Basket Analysis Transactional data Sparse matrix: thousands of columns, each row has only dozens of values Items Itemsets: transactions (TID) A most cited example “diapers and beer” 11/26/2018 CSE591: Data Mining by H. Liu

Association rule mining Finding interesting association or correlation relationships Defining interesting association rules Support (P(AB)) Confidence (P(B|A)) An association rule A -> B 11/26/2018 CSE591: Data Mining by H. Liu

Finding association rules Finding frequent itemsets downward closure property (or anti-monotonic) Finding association rules from frequent itemsets Frequent Itemsets minisup from 1-itemset to k-itemset Association rules miniconf satisfying minimum confidence 11/26/2018 CSE591: Data Mining by H. Liu

CSE591: Data Mining by H. Liu Apriori Level-wise search Anti-monotone property The procedure Join prune An example 11/26/2018 CSE591: Data Mining by H. Liu

CSE591: Data Mining by H. Liu Issues Efficiency Number of association rules size of data vs. size of association rules Post-processing Applications combining association rules with classification emergency patterns 11/26/2018 CSE591: Data Mining by H. Liu

Types of association rules Single dimensional association rules Multiple dimensional association rules Multi-level association rules 11/26/2018 CSE591: Data Mining by H. Liu