Abstract
In group pattern mining, we discover group patterns from a given user movement database based on their spatio-temporal distances. When both the number of users and the logging duration are large, group pattern mining algorithms become very inefficient. In this paper, we therefore propose a spherical location summarization method to reduce the overhead of mining valid 2-groups. In our experiments, we show that our group mining algorithm using summarized data may require much less execution time than that using non-summarized data.
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Wang, Y., Lim, EP., Hwang, SY. (2004). Efficient Group Pattern Mining Using Data Summarization. In: Lee, Y., Li, J., Whang, KY., Lee, D. (eds) Database Systems for Advanced Applications. DASFAA 2004. Lecture Notes in Computer Science, vol 2973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24571-1_78
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DOI: https://doi.org/10.1007/978-3-540-24571-1_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21047-4
Online ISBN: 978-3-540-24571-1
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