ZART : A Multifunctional Itemset Miner Algorithm
Identifieur interne : 006181 ( Main/Merge ); précédent : 006180; suivant : 006182ZART : A Multifunctional Itemset Miner Algorithm
Auteurs : Laszlo Szathmary ; Amedeo Napoli ; Sergei O. KuznetsovSource :
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Abstract
In this paper we address two main problems in data mining, namely the extraction of frequent patterns and the generation of interesting association rules. The most well-known algorithm for finding frequent patterns is the Apriori algorithm. The main problem with Apriori and most of its variations is the high number of operations required for counting pattern supports. To overpass this drawback, a new algorithm called Pascal was proposed, that introduced the notion of pattern counting inference. Using this technique, the support of a pattern can be determined without accessing the database if its so-called key patterns are already known. We propose a new algorithm, called Zart, which is based on Pascal and extends Pascal in certain ways : it can identify frequent closed itemsets, and in parallel it can find minimal generators of closed patterns. At present, there are no algorithms that propose to extract at the same time frequent itemsets, frequent closed itemsets, and minimal generators. We show how to use Zart to generate informative association rules. The reduced informative rules represent a minimal, non-redundant set of most relevant association rules without any loss of information. With the Zart algorithm these rules can be identified very quickly and easily.
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