MINING ASSOCIATION RULES FROM MARKET BASKET DATA USING SHARE MEASURES AND CHARACTERIZED ITEMSETS

Autor:	Robert J. Hilderman, Colin L. Carter, Howard J. Hamilton, Nick Cercone
Rok vydání:	1998
Předmět:	Apriori algorithm Association rule learning Knowledge extraction Artificial Intelligence Generalization Computer science Market basket InformationSystems_DATABASEMANAGEMENT Data mining computer.software_genre computer
Zdroj:	International Journal on Artificial Intelligence Tools. :189-220
ISSN:	1793-6349 0218-2130
DOI:	10.1142/s0218213098000111
Popis:	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 A priori algorithm for discovering association rules between items in large databases, and the A O G 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b4ac514a696d24186a51615e0540c9c7 https://doi.org/10.1142/s0218213098000111 Zobrazit plný text záznamu