A Scalable Approach for Improving Implementation of a Frequent Pattern Mining Algorithm using MapReduce Programming

A Frequent pattern is a pattern (a set of items, subsequences, sub-graphs, etc.) that occurs frequently in a transactional database. Frequent pattern mining gives vast benefit in domains such as extracting knowledge from transactional data for market basket analysis or cross-marketing and selling. A number of important FIM (Frequent itemset mining) algorithms have been developed to speed up mining performance since its inception. Unfortunately, when the dataset size is massive, it can still be prohibitively expensive for communication cost, memory usage, balanced data distribution & I/O utilization. One of the existing frequent pattern mining algorithms called CATS Tree (Compressed and Arranged Sequences tree) can perform interactive mining by a single scan. In this work, we propose to parallelize a part…