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2019 | OriginalPaper | Buchkapitel

Discovering Relationship Patterns Among Associated Temporal Event Sequences

verfasst von : Chao Han, Lei Duan, Zhangxi Lin, Ruiqi Qin, Peng Zhang, Jyrki Nummenmaa

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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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.

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Titel: Discovering Relationship Patterns Among Associated Temporal Event Sequences
verfasst von: Chao Han
Lei Duan
Zhangxi Lin
Ruiqi Qin
Peng Zhang
Jyrki Nummenmaa
Verlag: Springer International Publishing
Buch: Database Systems for Advanced Applications
Print ISBN: 978-3-030-18575-6

Electronic ISBN: 978-3-030-18576-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-18576-3_7

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