Mining for Mutually Exclusive Items in Transaction Databases

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Mining for Mutually Exclusive Items in Transaction Databases
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 paper, 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.
George Tzanis, Christos Berberidis
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JDWM
Authors George Tzanis, Christos Berberidis
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