Publication Details

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Appeared in: 2nd ADBIS Workshop on Data Mining and Knowledge Discovery, pp. 1-12, Thessaloniki, Greece, 2006.

Abstract: Mining a transaction database for association rules is a particularly popular data mining task, which involves the search for frequent co-occurrences among items. One of the problems often encountered is the large number of weak rules extracted. Item taxonomies, when available, can be used to reduce them to a more usable volume. In this paper we introduce a new data mining paradigm, which involves the discovery of pairs of mutually exclusive items. We call this new type of knowledge mutual exclusion, as opposed to association, and we propose its use to tackle the aforementioned problem. We formulate the problem of mining for mutually exclusive items, provide important background information, propose a novel mutual exclusion metric and finally, present a mining algorithm that we test on transaction data.

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