Fast Algorithms for Sequence Pattern Recognition in Massive Datasets

Abstract

Sequential Pattern Mining is the process of applying data mining techniques to a sequential database for the purpose of discovering the correlation relationships that exist among an ordered list of events. The patterns can be used to focus on the retailing industry, including attached mailing, add-on sales and customer satisfaction. In this vaper, I present fast and efficient algorithms called AprioriAllSID and GSPSID for mining sequential patterns that are fundamentally different from known algorithms like Apriori All and GSP (Generalized Sequential Patterns). The algorithm has been implemented on an experimental basis and its performance studied. The performance study shows that the proposed algorithms have an excellent performance over the best existing algorithms.