Mining of Frequent Item with BSW Chunking

Abstract

Apriori is an algorithm for finding the frequent patterns in transactional databases is considered as one of the most important data mining problems. Apriori algorithm is a masterpiece algorithm of association rule mining. This algorithm somehow has constraint and thus, giving the opportunity to do this research. Increased availability of the Multicore processors is forcing us to re-design algorithms and applications so as to accomplishment the computational power from multiple cores finding frequent item sets is more expensive in terms of computing resources utilization and CPU power. Thus superiority of parallel apriori algorithms effect on parallelizing the process of frequent item set find. The parallel frequent item sets mining algorithms gives the direction to solve the issue of distributing the candidates among processors. Efficient algorithm to discover frequent patterns is important in data mining research Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. The objective of the Apriori Algorithm is to find associations between different sets of data. It is occasionally referred to as "Market Basket Analysis". Every several set of data has a number of items and is called a transaction. The achievement of Apriori is sets of rules that tell us how often items are contained in sets of data. In order to find more valuable rules, our basic aim is to implement apriori algorithm using multithreading approach which can utilization our system hardware power to improved algorithm is reasonable and effective, can extract more value information.