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• W2963676109 abstract "This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps versus skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance." @default.
• W2963676109 created "2019-07-30" @default.
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• W2963676109 date "2017-10-11" @default.
• W2963676109 modified "2023-10-14" @default.
• W2963676109 title "Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks" @default.
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• W2963676109 doi "https://doi.org/10.1007/978-3-319-65406-5_3" @default.
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