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Incremental maintenance of generalized association rules under taxonomy evolution

Institute of Information Engineering, I-Shou University, Kaohsiung 840, Taiwan

Wen-Yang Lin

Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, wylin{at}nuk.edu.tw

Rong Jeng

Department of Information Management, I-Shou University, Kaohsiung 840, Taiwan

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of items remained unchanged, disregarding the fact that the taxonomy might be updated as new transactions are added to the database over time. If this happens, effectively updating the generalized association rules to reflect the database change and related taxonomy evolution is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called IDTE and IDTE2, which can incrementally update the generalized association rules when the taxonomy of items evolves as a result of new transactions. Empirical evaluations show that our algorithms can maintain their performance even for large numbers of incremental transactions and high degrees of taxonomy evolution, and are faster than applying contemporary generalized association mining algorithms to the whole updated database.

Key Words: data mining • frequent itemsets • generalized association rules • incremental maintenance • taxonomy evolution

This version was published on April 1, 2008

Journal of Information Science, Vol. 34, No. 2, 174-195 (2008)
DOI: 10.1177/0165551507082271


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