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Mining for Mutually Exclusive Items in Transaction Databases

Author

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  • George Tzanis

    (Aristotle University of Thessaloniki, Greece)

  • Christos Berberidis

    (Aristotle University of Thessaloniki, Greece)

Abstract

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

Suggested Citation

  • George Tzanis & Christos Berberidis, 2007. "Mining for Mutually Exclusive Items in Transaction Databases," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 45-59, July.
    • Handle: RePEc:igg:jdwm00:v:3:y:2007:i:3:p:45-59
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