Mining market basket data using share measures and characterized itemsets [chapter]

Robert J. Hilderman, Colin L. Carter, Howard J. Hamilton, Nick Cercone
1998 Lecture Notes in Computer Science  
A b s t r a c t . We propose the share-confidence framework for knowledge discovery from databases which addresses the problem of mining itemsets from market basket data. Our goal is two-fold: (1) to present new itemset measures which are practical and useful alternatives to the commonly used support measure; (2) 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
more » ... 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 suggest 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 shareconfidence framework.
doi:10.1007/3-540-64383-4_14 fatcat:snuokc5p55daxgy25ihhsq2x7i