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• W2777449345 abstract "In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification. Code related to this chapter is available at: http://feb.kuleuven.be/public/u0092789/ ." @default.
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• W2777449345 date "2017-01-01" @default.
• W2777449345 modified "2023-09-24" @default.
• W2777449345 title "Behavioral Constraint Template-Based Sequence Classification" @default.
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• W2777449345 doi "https://doi.org/10.1007/978-3-319-71246-8_2" @default.
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