Abstract
In this paper we present extensions for continuous pattern mining. Our previous continuous pattern mining algorithm mines the set of all frequent sequences satisfying the minSup condition. However, those sequences contain an explosive number of frequent subsequences, which makes the analysis and understanding of patterns very difficult. In order to overcome these difficulties, we propose four new algorithms for mining maximal and closed continuous patterns. These algorithms return a superset of the result patterns and then a post-pruning algorithm is performed to eliminate redundant sequences. For each type of patterns (maximal or closed) two algorithms are presented (with and without some improvements). The key idea is to omit as many redundant sequences as possible during the exploration. The proposed algorithms allow one to reduce the size of the result set when input sequences have low uniqueness.
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Gorawski, M., Jureczek, P. (2011). Extensions for Continuous Pattern Mining. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_24
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DOI: https://doi.org/10.1007/978-3-642-23878-9_24
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