Mining Association Rules in Large Databases using localized pattern

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Kanhaiya Lal
Dr. N.C. Mahanti


Discovering association rules from very large databases is an important data mining problem. In this paper we propose an algorithm
which is applicable to large databases and can be used efficiently for discovering important rules. It discovers the large itemsets without multiple
passes over the database. In this paper we concentrate over the discovery of localized patterns in a sub-domain, which can be easily processed to
obtain large-itemsets and valid rules, consecutively. We present an efficient algorithm for mining association rules that is faster than the
previously proposed partition algorithms. The algorithm is also ideally suited for parallelization.

Keywords: Clustered-Domain; Localized-Pattern; Parallelizable Mining; Domain partitioning; Generalized Association Rule.


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