Generalized multidimensional association rules

Aoying Zhou, Shuigeng Zhou, Wen Jin, Zengping Tian

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of research activities around this problem. Traditional association rule mining is limited to intra-transaction. Only recently the concept of N-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extends Lu's definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, which is more general, flexible and reasonable than NDITAR.

Original languageEnglish
Pages (from-to)388-392
Number of pages5
JournalJournal of Computer Science and Technology
Volume15
Issue number4
DOIs
StatePublished - Jul 2000
Externally publishedYes

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