An efficient approach for mining sequential patterns using multiple threads on very large databases

Abstract

Sequential pattern mining (SPM) plays an important role in data mining, with broad applications such as in financial markets, education, medicine, and prediction. Although there are many efficient algorithms for SPM, the mining time is still high, especially for mining sequential patterns from huge databases, which require the use of a parallel technique. In this paper, we propose a parallel approach named MCM-SPADE (Multiple threads CM-SPADE), for use on a multi-core processor system as a :multi-threading technique for SPM with very large database, to enhance the performance of the previous methods SPADE and CM-SPADE. The proposed algorithm uses the vertical data format and a data structure named CMAP (Co-occurrence MAP) for storing co-occurrence information. Based on the data structure CMAP, the proposed algorithm performs early pruning of the candidates to reduce the search space and it partitions the related tasks to each processor core by using the divide-and-conquer property. The proposed algorithm also uses dynamic scheduling to avoid task idling and achieve load balancing between processor cores. The experimental results show that MCM-SPADE attains good parallelization efficiency on various input databases.