Many practical applications are related to frequent sequential pattern mining, ranging from Web Usage Mining to Bioinformatics. To ensure an appropriate extraction cost for useful mining tasks, a key issue is to push the user-defined constraints deep inside the mining algorithms. In this paper, we study the search for frequent sequential patterns that are also similar to an user-defined reference pattern. While the effective processing of the frequency constraints is well-understood, our contribution concerns the identification of a relaxation of the similarity constraint into a convertible anti-monotone constraint. Both constraints are then used to prune the search space during a levelwise search. Preliminary experimental validations have confirmed the algorithm efficiency.
CITATION STYLE
Capelle, M., Masson, C., & Boulicaut, J. F. (2002). Mining frequent sequential patterns under a similarity constraint. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2412, pp. 1–6). Springer Verlag. https://doi.org/10.1007/3-540-45675-9_1
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