Efficient search of reliable exceptions

Huan Liu, Hongjun Lu, Ling Feng, Farhad Hussain

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

43 Scopus citations


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. While such strong patterns are helpful in prediction, the unexpectedness and contradiction exhibited by weak patterns are also very useful although they represent a relatively small number of objects. In this paper, we address the problem of finding weak patterns (i.e., reliable exceptions) from databases. A simple and efficient 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 set of real-life data sets, and present interesting findings.

Original languageEnglish (US)
Title of host publicationMethodologies for Knowledge Discovery and Data Mining - 3rd Pacific-Asia Conference, PAKDD 1999, Proceedings
EditorsNing Zhong, Lizhu Zhou
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540658661, 9783540658665
StatePublished - 1999
Externally publishedYes
Event3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999 - Beijing, China
Duration: Apr 26 1999Apr 28 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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