An Efficient Algorithm for Mining Maximal Frequent Item Sets

Abstract

Problem Statement: In today's life, the mining of frequent patterns is a basic problem in data mining applications. The algorithms which are used to generate these frequent patterns must perform efficiently. The objective was to propose an effective algorithm which generates frequent patterns in less time. Approach: We proposed an algorithm which was based on hashing technique and combines a vertical tidset representation of the database with effective pruning mechanisms. It removes all the non-maximal frequent item-sets to get exact set of MFI directly. It worked efficiently when the number of item-sets and tid-sets is more. Results: The performance of our algorithm had been compared with recently developed MAFIA algorithm and the results show how our algorithm gives better performance. Conclusions: Hence, the proposed algorithm performs effectively and generates frequent patterns faster.