Novel Frequent Pattern Mining Algorithm Based on Parallelization Scheme

Abstract

Frequent pattern mining (FPM) is a very important technique in data mining and has attracted a wide range of practical applications. Equivalent Class Clustering (Eclat) has been identified as one of the most efficient FPM algorithm. We present P-Eclat, a novel parallel FPM algorithm which is an improvement of the Eclat algorithm, where a partial breadth-first search is employed to achieve maximum parallelism. Our approach uses a TIDset representation of the vertical transaction lists across multiple threads on a CPU. Current parallelization techniques for mining frequent patterns don’t fully utilize benefits accrued from multi core shared memory machines. Our parallel mining approach reduces the synchronization requirements, maximizing independence of data and enhances scalability. We also introduce several optimization techniques to improve the algorithm’s performance. Experimental results show that P-Eclat algorithm outperforms both Eclat and dEclat algorithms.