A Scalable Sequential Pattern Mining Algorithm

Sequential pattern mining is a technique used to discoverfrequentsubsequencesaspatternsina sequencedatabase. Many excellent sequential pattern mining approaches such as GSP, SPADE, and PrefixSpan have been proposed. However, the existing approaches still encounter problems when the set of all different items in a sequence database is large. Scalability with respect to not only the varying size of a sequence database, but also the varying size of the set of all different items is crucial for many applications. In this paper we address the subject of mining frequent sequential patterns in the large sequence database with numerous kinds of items. An effective algorithm for the purpose, called SSPM (ScalableSequentialPattern Mining), is proposed. SSPM is characterized by its fast convergence, meaning that the search space will shrink quickly as mining operation proceeds. SSPM does not limit the number of different items in a sequence database, and thus it has better scalability, and can be applicable to a large scale of sequence databases. The experimental and analytical results demonstrated that SSPM is effective and is faster than conventional algorithms in the above stated cases.