Generalizing the notion of confidence

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

9 Scopus citations


In this paper, we explore extending association analysis to non-traditional types of patterns and non-binary data by generalizing the notion of confidence. The key idea is to regard confidence as a measure of the extent to which the strength of one association pattern provides information about the strength of another. This approach provides a framework that encompasses the traditional concept of confidence as a special case and can be used as the basis for designing a variety of new confidence measures. Besides discussing such confidence measures, we provide examples that illustrate the potential usefulness of a generalized notion of confidence. In particular, we describe an approach to defining confidence for error tolerant itemsets that preserves the interpretation of confidence as a conditional probability and derive a confidence measure for continuous data that agrees with the standard confidence measure when applied to binary transaction data.

Original languageEnglish (US)
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Number of pages8
StatePublished - 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: Nov 27 2005Nov 30 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other5th IEEE International Conference on Data Mining, ICDM 2005
Country/TerritoryUnited States
CityHouston, TX


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