Parallel Bidirectional Approach for Eclat Algorithm in Association Rule Mining

Abstract

Data mining is used to deal with the huge size of the data stored in the database to extract the desired information and knowledge from the database. There are various methods for extraction of data like association, clustering, classification etc. Association is one of the most efficient data mining method among them. There are various algorithms like Apriori, FP-Growth, and Eclat etc. Among them Eclat algorithm is the best fastest algorithm and find the frequent pattern. But in Eclat algorithm, there are few issues like scanning all dataset, consume more memory and do not handle large dataset. So to reduce this issue and present new algorithm is call Bi-Eclat. Bi-Eclat can reduce the scanning of dataset but it can’t handle the large dataset and consume more memory. So here the given mentioned algorithm is Parallel Eclat algorithm, which gives better results compared to Bi-Eclat method.