Efficient constraint-based sequential pattern mining using dataset filtering techniques

Basic formulation of the sequential pattern discovery problem assumes that the only constraint to be satisfied by discovered patterns is the minimum support threshold. However, very often users want to restrict the set of patterns to be discovered by adding extra constraints on the structure of patterns. Data mining systems should be able to exploit such constraints to speed-up the mining process. In this paper we discuss efficient constraint-based sequential pattern mining using dataset filtering techniques. We show how to transform a given data mining task into an equivalent one operating on a smaller dataset. We present an extension of the GSP algorithm using dataset filtering techniques and experimentally evaluate performance gains offered by the proposed method.