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Discovering frequent closed itemsets for association rules

Abstract : In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice rather than the subset lattice. Moreover, we show that the set of all frequent closed itemsets suffices to determine a reduced set of association rules, thus addressing another important data mining problem: limiting the number of rules produced without information loss. We propose a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets. We realized experiments to compare our approach to the commonly used frequent itemset search approach. Those experiments showed that our approach is very valuable for dense and/or correlated data that represent an important part of existing databases.
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Contributor : Nicolas Pasquier <>
Submitted on : Monday, April 26, 2010 - 1:03:41 PM
Last modification on : Monday, January 20, 2020 - 12:12:05 PM
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  • HAL Id : hal-00467747, version 1

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Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lotfi Lakhal. Discovering frequent closed itemsets for association rules. ICDT'1999 International Conference on Database Theory, Jan 1999, Jerusalem, Israel. pp.398-416. ⟨hal-00467747⟩

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