Sequential pattern mining has now become an important data mining problem. For many practical applications, the users may be only interested in those sequential patterns satisfying some constraints expressing their interest. The proposed constraints in general can be categorized into four classes, among which monotony and tough constraints are the most difficult ones to be processed. However, many of the available algorithms are proposed for some special constraints based sequential pattern mining. It is thus difficult to be adapted to other classes of constraints. In this paper we propose a new general framework called CBPSAlgm based on the projection-based pattern growth principal. Under this framework, ineffective item pruning strategies are designed and integrated to construct effective algorithms for monotony and tough constraint based sequential pattern mining. Experimental results show that our proposed methods outperform other algorithms.
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