Mining periodic behaviors for moving objects

Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays, Peter Nye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

175 Citations (Scopus)

Abstract

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationKDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
Pages1099-1108
Number of pages10
DOIs
StatePublished - Sep 7 2010
Event16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010 - Washington, DC, United States
Duration: Jul 25 2010Jul 28 2010

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010
CountryUnited States
CityWashington, DC
Period7/25/107/28/10

Fingerprint

Autocorrelation
Fourier transforms
Statistical Models

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Cite this

Li, Z., Ding, B., Han, J., Kays, R., & Nye, P. (2010). Mining periodic behaviors for moving objects. In KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data (pp. 1099-1108). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1835804.1835942
Li, Zhenhui ; Ding, Bolin ; Han, Jiawei ; Kays, Roland ; Nye, Peter. / Mining periodic behaviors for moving objects. KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data. 2010. pp. 1099-1108 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
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Li, Z, Ding, B, Han, J, Kays, R & Nye, P 2010, Mining periodic behaviors for moving objects. in KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1099-1108, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010, Washington, DC, United States, 7/25/10. https://doi.org/10.1145/1835804.1835942

Mining periodic behaviors for moving objects. / Li, Zhenhui; Ding, Bolin; Han, Jiawei; Kays, Roland; Nye, Peter.

KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data. 2010. p. 1099-1108 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Li Z, Ding B, Han J, Kays R, Nye P. Mining periodic behaviors for moving objects. In KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data. 2010. p. 1099-1108. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1835804.1835942