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An Efficient Reduced Pattern Count Tree Method for Discovering Most Accurate Set of Frequent itemsets


Geetha M, R. J. D’Souza


Vol. 8  No. 8  pp. 121-126


Discovery of association rules from large volumes of data is an important Data Mining problem. In this paper a novel method for discovering most accurate set of frequent itemsets using partition algorithm is implemented. This method uses the concept of Reduced Pattern Count Tree, Ladder merging and Reduced Minimum Support to discover most accurate set of frequent itemsets in a single scan of database. This algorithm is a modified version of the existing Partition Algorithm, but leads to the significant reduction in time and disk input/ output, and has lower memory requirements as compared to some of the Existing algorithms.


Frequent itemset, Path, Reduced minimum Support, Reduced Pattern Count Tree