Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets

We propose the share-conndence framework for knowledge discovery from databases which addresses the problem of mining characterized association rules from market basket data (i.e., itemsets). Our goal is to not only discover the buying patterns of customers, but also to discover customer prooles by partitioning customers into distinct classes. We present a new algorithm for classifying itemsets based upon characteristic attributesextractedfrom census or lifestyle data. Our algorithmcombinesthe Apriori algorithm for discovering association rules between items in large databases, and the AOG algorithm for attribute-oriented generalization in large databases. We show how characterized itemsets can be generalized according to concept hierarchies associated with the characteristicattributes. Finally, we present experimental results that demonstrate the utility of the share-conndence framework.