Abstract
Distinguishing sequential pattern (DSP) mining has been widely employed in many applications, such as building classifiers and comparing/analyzing protein families. However, in previous studies on DSP mining, the gap constraints are very rigid – they are identical for all discovered patterns and at all positions in the discovered patterns, in addition to being predetermined. This paper considers a more flexible way to handle gap constraint, allowing the gap constraints between different pairs of adjacent elements in a pattern to be different and allowing different patterns to use different gap constraints. The associated DSPs will be called DSPs with flexible gap constraints. After discussing the importance of specifying/determining gap constraints flexibly in DSP mining, we present GepDSP, a heuristic mining method based on Gene Expression Programming, for mining DSPs with flexible gap constraints. Our empirical study on real-world data sets demonstrates that GepDSP is effective and efficient, and DSPs with flexible gap constraints are more effective in capturing discriminating sequential patterns.
This work was supported in part by NSFC 61572332, and China Postdoctoral Science Foundation 2014M552371.
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We can set the values of \(\mathcal {G}_{min}\) and \(\mathcal {G}_{max}\) to 0 and the maximal sequence length (of the dataset under consideration), respectively, as default.
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Gao, C., Duan, L., Dong, G., Zhang, H., Yang, H., Tang, C. (2016). Mining Top-k Distinguishing Sequential Patterns with Flexible Gap Constraints. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_7
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DOI: https://doi.org/10.1007/978-3-319-39937-9_7
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