Mining weak rules

Huan Liu, Hongjun Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Finding patterns from data sets is a fundamental task of data mining. If we categorize all patterns into strong, weak, and random, conventional data mining techniques are designed only to find strong patterns, which hold for numerous objects and are usually consistent with the expectations of experts. In this paper, we address the problem of finding weak patterns (i.e., reliable exceptions) from databases. They are valid for a small number of objects. A simple approach is proposed which uses deviation analysis to identify interesting exceptions and explore reliable ones. Besides, it is flexible in handling both subjective and objective exceptions. We demonstrate the effectiveness of the proposed approach through a benchmark data set.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Computer Society's International Computer Software and Applications Conference
PublisherIEEE
Pages309-310
Number of pages2
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1999 23rd Annual International Computer Software and Applications Conference (COMPSAC '99) - Phoenix, AZ, USA
Duration: Oct 27 1999Oct 29 1999

Other

OtherProceedings of the 1999 23rd Annual International Computer Software and Applications Conference (COMPSAC '99)
CityPhoenix, AZ, USA
Period10/27/9910/29/99

ASJC Scopus subject areas

  • Software

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  • Cite this

    Liu, H., & Lu, H. (1999). Mining weak rules. In Proceedings - IEEE Computer Society's International Computer Software and Applications Conference (pp. 309-310). IEEE.