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An Improved Frequent Itemset Generation Algorithm Based On Correspondence

Authors

Ajay R Y, Sharath Kumar A, Preetham Kumar and Radhika M. Pai, Manipal University, India

Abstract

Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by thenumber of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scanalgorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database.

Keywords

Maximal Frequent Itemset, Support, Data Mining, Mapping.

Full Text  Volume 2, Number 5