Abstract
Sequential pattern mining is a challenging problem that has received much attention in the past few decades. The mining of large sequential databases can be very time consuming and produces a large number of unrelated patterns that must be evaluated. In this paper, we explore the problems of frequent prefix, prefix-closed, and prefix-maximal pattern mining along with their suffix variants. By constraining the pattern mining task, we are able to reduce the mining time required while obtaining patterns of interest. We introduce notations related to prefix/suffix sequential pattern mining while providing theorems and proofs that are key to our proposed algorithms. We show that the use of projected databases can greatly reduce the time required to mine the complete set of frequent prefix/suffix patterns, prefix/suffix-closed patterns, and prefix/suffix-maximal patterns. Theoretical analysis shows that our approach is better than the current existing approach, and empirical analysis on various datasets is used to support these conclusions.
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Singh, R., Graves, J.A., Talbert, D.A., Eberle, W. (2018). Prefix and Suffix Sequential Pattern Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_24
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DOI: https://doi.org/10.1007/978-3-319-96136-1_24
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