Abstract
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.
Research partially funded by the European contract cInQ IST 2000-26469.
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Capelle, M., Masson, C., Boulicaut, JF. (2002). Mining Frequent Sequential Patterns under a Similarity Constraint. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_1
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DOI: https://doi.org/10.1007/3-540-45675-9_1
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