The Local Power Set in Transaction Scanning for Efficient Frequent Item sets Mining

Abstract

Discovering frequent item sets has been known among researchers as the most computationally expensive task in association rules mining. As a result of a technique used in the traditional algorithm, Apriori, generating new candidate (k+1)-item sets by performing self-join operation over elements of the frequent (k)-item sets which needs to scan dataset multiple times. Although, many modern algorithms have been proposed to the problems, most of them encounter with wasting the times to manipulate other data structures in memory. In this paper, we propose an algorithm utilizing concept of local power set enumeration and hash table. The proposed is not only able to escape self-join operation but also able to reduce CPU-times in computation when compared to the related algorithm that also applied concept of power set. To get better performance, we also modify the algorithm to two new versions applying an intersection-set operation in the phase of removing useless item sets from the dataset, instead of checking item elements of transactions one by one. Many experiments are conducted to evaluate performance of all our proposed. The results expressed that all modified versions of the algorithms provide better performance in term of reducing computational CPU-time and take less amount of scanning.