Performance Comparison of Frequent Pattern Mining Algorithms for Business Intelligence Analytics

Abstract

Objectives: In this paper, a simple and flexible partition algorithm has been proposed to mine frequent data item sets. This partition algorithm is different from other frequent pattern mining algorithm like Apriori algorithm, AprioriAllHybrid algorithm etc. Method: Partition algorithm concept has been proposed to increase the execution speed with minimum cost. Initially only for one time the database is scanned and separate partitions will be created for each sets of itemsets, which is 1-itemset, 2-itemsets, 3-itemsets etc. Findings: The scanning of whole database is not necessary to get the count of an itemset, it is enough to get the count of each data itemsets from its partition. This partition algorithm approach is implemented and evaluated against AprioriAllHybrid and Apriori algorithm. The candidate itemsets generated at each step is reduced and the scanning time is also reduced. The proposed methodology performance is significantly better than other algorithms and it promotes the faster execution time for mining frequent patterns. Applications: This proposed algorithm is used in areas like retail sales, production, universities, finance, banking systems and for business to plan and estimate the future values.