Mining conditional discriminative sequential patterns

Abstract

Discriminative sequential pattern mining is one of the most important topics in pattern mining, which has a very wide range of applications. Discriminative sequential pattern mining is intended to extract sequential patterns with significant differences among different classes. In recent years, a variety of algorithms for mining discriminative sequential patterns have been proposed, but these algorithms still suffer from generating many redundant patterns. There are many factors that may lead to the redundancy of reported patterns, among which the subset-induced redundancy is the most critical one, i.e., some patterns are reported to be discriminative mainly because some of their sub-patterns are strongly discriminative. In order to solve the subset-induced redundancy issue, we propose the concept of conditional discriminative sequential pattern, and design a new algorithm called CDSPM (Conditional Discriminative Sequential Pattern Mining) for extracting such kinds of patterns. The experimental results on real data sets show that CDSPM can effectively remove discriminative sequential patterns that are redundant with respect to their sub-patterns.