A novel single scan distributed pattern mining algorithm for frequent pattern identification

Abstract

In data mining, the extraction of frequent patterns from large databases is still a challenging and difficult task due to the various drawbacks such as, high response time, communication cost to alleviates such issues, a new algorithm namely single scan distributed pattern mining algorithm (SSDPMA) is proposed in this paper for frequent mining. The frequent patterns are extracted in a single scan of the database. Then, it is split into multiple files, which will be shared to multiple virtual machines (VMs) to store and compute the weight for the distinct records. Then, the support, confidence and threshold values are estimated. If the limit is greater than the given data, the frequent data are mined by using the proposed SSDPMA algorithm. The experimental results evaluate the performance of the proposed system in terms of response time, message size, execution time, run time and memory usage.