Efficient mining of categorized association rules in large databases

S. Tseng*

*Corresponding author for this work

Research output: Contribution to journalConference article

Abstract

A number of studies have been made on discovering association rules in a large database due to the wide applications. The common goal of the studies focused on finding the associated occurrence patterns between all items in a database. In practice, mining the association rules with the granularity as fine as an item could result in a huge number of rules that are too large to utilize efficiently. In practical applications, the users may be more interested in the associations between the categories the items belong to. In this paper, we propose a new method for mining categorized association rules efficiently by using compressed feature vectors. With the proposed method, at most one scan of the database is needed to produce the categorized association rules in each user query even under different mining parameters. Furthermore, the calculation time during the mining process is also reduced greatly by using only the simple logic operations on feature vectors. Hence, the overall performance in mining categorized association rules could be improved substantially.

Original languageEnglish
Pages (from-to)3606-3610
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume5
DOIs
StatePublished - 1 Dec 2000
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 8 Oct 200011 Oct 2000

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