Abstract. We introduce the measures share, coincidence and dominance as alternatives to the standard itemset methodology measure of support. An itemset is a group of items bought together in a transaction. The support of an itemset is the ratio of transactions containing the itemset to the total number of transactions. The share of an itemset is the ratio of the count of items purchased together to the total count of items in all transactions. The coincidence of an itemset is the ratio of the count of items in that itemset to the total of those same items in the database. The dominance of an item in an itemset specifies the extent to which that item dominates the total of all items in the itemset. Share based measures have the advantage over support of reflecting accurately how many units are being moved by a business. The share measure can be extended to quantify the financial impact of an itemset on the business.

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We propose the share-confidence 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 profiles by partitioning customers into distinct classes. We present a new algorithm for classifying itemsets based upon characteristic attributes extracted from census or lifestyle data. Our algorithm combines the 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 characteristic attributes. Finally, we present experimental results that demonstrate the utility of the share-confidence framework.