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Mining Un-interpreted Generalized Association Rules by Linear Inequalities

Descriptive/Deductive Data Mining Approach
  • Tsau Young Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

Taking the spirit of descriptive statistic methods data mining is viewed as a deductive science, no inductive generalizations or predicative assertions. We call this approach descriptive/deductive data mining (DDM) to stress spirit of descriptive statistic methods and the role of mathematical deductions.

Such a seemingly restrictive methodology, somewhat surprisingly, turns out to be quite far reaching. Previously, we have observed in ICDM02 that (1) Isomorphic relations have isomorphic patterns (classical association rules). This observation implies, from data mining prospect, that relations and patterns are syntactic in nature. We also have reported that (2) attributes or features (including un-interpreted ones) of a given relation can be enumerated mathematically, though, in intractable time. In this paper, we proved (3) generalized association rules (including un-interpreted rules) can be discovered by solving a finite set of integral linear inequalities within polynomial time.

Keywords

association rules attribute feature bitmap indexes granular data model data mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tsau Young Lin
    • 1
  1. 1.Department of Computer ScienceSan Jose State UniversitySan JoseUSA

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