Abstract
Sequential data mining is prevalent in many real world applications, such as gene sequence analysis, consumer shopping log analysis, social networking analysis, and banking transaction analysis. Contrast sequence data mining is useful in describing the differences between two sets (classes) of sequences. However, in prior studies, little work has been done in how to mine the patterns from sequences formed by associated temporal events, where there exist relationships in chronological order between any two events in a sequence. To fill this gap, we consider the problem of mining associated temporal relationship pattern (ATRP) and propose a method, called ATTEND (AssociaTed Temporal rElationship patterN Discovery), to discover ATRPs with top contrast measure from two sets of associative temporal event sequences. Moreover, we design several heuristic strategies to improve the efficiency of ATTEND. Experiments on both real and synthetic data demonstrate that ATTEND is effective and efficient.
This work was supported in part by NSFC 61572332, the Fundamental Research Funds for the Central Universities 2016SCU04A22, and the China Postdoctoral Science Foundation 2016T90850.
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Han, C., Duan, L., Lin, Z., Qin, R., Zhang, P., Nummenmaa, J. (2019). Discovering Relationship Patterns Among Associated Temporal Event Sequences. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_7
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