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Title: Mining for Mutually Exclusive Items in Transaction Databases
Author(s): G. Tzanis, C. Berberidis.
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Appeared in: International Journal of Data Warehousing and Mining, David Taniar (Ed.), Idea Group Publishing, 3(3), 2007.
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 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.
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        This paper has been cited by the following:

1 T. Hamrouni, S. Ben Yahia, and E. Mephu Nguifo, GARM: Generalized Association Rule Mining. In Proceedings of the 6th Conference on Concept Lattices and Their Applications, Olomouc, Czech Republic, pp. 145-156, 2008.
2 N. Gorla, P.W.Y. Betty. Vertical Fragmentation in Databases Using Data-Mining Technique. International Journal of Data Warehousing and Mining, 4(3), pp. 35-53, 2008.
3 T. Hamrouni, S. Ben Yahia, and E. Mephu Nguifo, Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets, Data and Knowledge Engineering, 2009.


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