Itemset Trees for Targeted Association Querying

Miroslav Kubat, Alaaeldin Hafez, Vijay V. Raghavan, Jayakrishna R. Lekkala, Wei Kian Chen

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn these groups into business-oriented rules. Previous research has focused predominantly on how to obtain exhaustive lists of such associations. However, users often prefer a quick response to targeted queries. For instance, they may want to learn about the buying habits of customers that frequently purchase cereals and fruits. To expedite the processing of such queries, we propose an approach that converts the market-basket database into an itemset tree. Experiments indicate that the targeted queries are answered in a time that is roughly linear in the number of market baskets, N. Also, the construction of the itemset tree has O(N) space and time requirements. Some useful theoretical properties are proven.

Original languageEnglish (US)
Pages (from-to)1522-1534
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume15
Issue number6
DOIs
StatePublished - Nov 1 2003

Keywords

  • Association mining
  • Data mining
  • Market baskets

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Information Systems

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