Mining top-k distinguishing sequential patterns with flexible gap constraints

Abstract

Distinguishing sequential pattern (DSP) mining has been widely employed in many applications, such as building classifiers and comparing/analyzing protein families. However, in previous studies on DSP mining, the gap constraints are very rigid – they are identical for all discovered patterns and at all positions in the discovered patterns, in addition to being predetermined. This paper considers a more flexible way to handle gap constraint, allowing the gap constraints between different pairs of adjacent elements in a pattern to be different and allowing different patterns to use different gap constraints. The associated DSPs will be called DSPs with flexible gap constraints. After discussing the importance of specifying/determining gap constraints flexibly in DSP mining, we present GepDSP, a heuristic mining method based on Gene Expression Programming, for mining DSPs with flexible gap constraints. Our empirical study on real-world data sets demonstrates that GepDSP is effective and efficient, and DSPs with flexible gap constraints are more effective in capturing discriminating sequential patterns.