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Mining Market Basket Data Using Share Measures and Characterized Itemsets (1998)
| Venue: | Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'98 |
| Citations: | 20 - 7 self |
BibTeX
@INPROCEEDINGS{Hilderman98miningmarket,
author = {Robert J. Hilderman and Colin L. Carter and Howard J. Hamilton and Nick Cercone},
title = {Mining Market Basket Data Using Share Measures and Characterized Itemsets},
booktitle = {Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'98},
year = {1998},
pages = {159--173}
}
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Abstract
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 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 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. 1
Keyphrases
characterized itemsets characteristic attribute large database share-confidence framework new itemset measure algorithm combine lifestyle data knowledge discovery apriori algorithm attribute-oriented generalization market basket data buying pattern used support measure useful alternative distinct class association rule shareconfidence framework new algorithm experimental result customer profile aog algorithm
