A parallel algorithm for mining multiple partial periodic patterns

Abstract

The mining of partial periodic patterns is an interesting type of data mining that is widely used in the analysis of markets, such as for stock management and sales management. However, the existence of huge data sets make the scalability of data-mining algorithms a very important objective, and in recent years parallel computing has been applied to general data-mining algorithms. This paper addresses the problem of mining multiple partial periodic patterns in a parallel computing environment. To reduce the cost of communication between the processors, our approach employs the independence property of prime numbers to classify partial periodic patterns into multiple independent sets. Moreover, a novel method of distributing mining tasks among the processors is proposed. A set of simulations is used to demonstrate the benefits of our approach.