Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization

Takeshi Fukuda, Yasuhiko Morimoto, Takeshi Tokuyama, Shinichi Morishita

Research output: Contribution to journalArticlepeer-review

191 Citations (Scopus)

Abstract

We discuss data mining based on association rules for two numeric attributes and one Boolean attribute. For example, in a database of bank customers, "Age" and "Balance" are two numeric attributes, and "CardLoan" is a Boolean attribute. Taking the pair (Age, Balance) as a point in two-dimensional space, we consider an association rule of the form ((Age, Balance) ∈ P) ⇒ (CardLoan = Yes), which implies that bank customers whose ages and balances fall in a planar region P tend to use card loan with a high probability. We consider two classes of regions, rectangles and admissible (i.e. connected and x-monotone) regions. For each class, we propose efficient algorithms for computing the regions that give optimal association rules for gain, support, and confidence, respectively. We have implemented the algorithms for admissible regions, and constructed a system for visualizing the rules.

Original languageEnglish
Pages (from-to)13-23
Number of pages11
JournalSIGMOD Record (ACM Special Interest Group on Management of Data)
Volume25
Issue number2
DOIs
Publication statusPublished - 1996 Jun

ASJC Scopus subject areas

  • Software
  • Information Systems

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