Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases

  • Hardy Kremer
  • Stephan Günnemann
  • Arne Held
  • Thomas Seidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)


Mining temporal multivariate data by clustering techniques is recently gaining importance. However, the temporal data obtained in many of today’s applications is often complex in the sense that interesting patterns are neither bound to the whole dimensional nor temporal extent of the data domain. Under these conditions, patterns mined by existing multivariate time series clustering and temporal subspace clustering techniques cannot correctly reflect the true patterns in the data.

In this paper, we propose a novel clustering method that mines temporal coherent subspace clusters. In our model, these clusters are reflected by sets of objects and relevant intervals. Relevant intervals indicate those points in time in which the clustered time series show a high similarity. In our model, each dimension has an individual set of relevant intervals, which together ensure temporal coherence. In the experimental evaluation we demonstrate the effectiveness of our method in comparison to related approaches.


Time Series Temporal Coherence Subspace Cluster Multivariate Time Series Temporal Extent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aggarwal, C.C., Procopiuc, C.M., Wolf, J.L., Yu, P.S., Park, J.S.: Fast algorithms for projected clustering. In: ACM SIGMOD, pp. 61–72 (1999)Google Scholar
  2. 2.
    Dasu, T., Swayne, D.F., Poole, D.: Grouping Multivariate Time Series: A Case Study. In: IEEE ICDMW, pp. 25–32 (2005)Google Scholar
  3. 3.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2), 1542–1552 (2008)Google Scholar
  4. 4.
    Fu, T.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24(1), 164–181 (2011)CrossRefGoogle Scholar
  5. 5.
    Günnemann, S., Färber, I., Müller, E., Assent, I., Seidl, T.: External evaluation measures for subspace clustering. In: ACM CIKM, pp. 1363–1372 (2011)Google Scholar
  6. 6.
    Hu, Z., Bhatnagar, R.: Algorithm for discovering low-variance 3-clusters from real-valued datasets. In: IEEE ICDM, pp. 236–245 (2010)Google Scholar
  7. 7.
    Jiang, D., Pei, J., Ramanathan, M., Tang, C., Zhang, A.: Mining coherent gene clusters from gene-sample-time microarray data. In: ACM SIGKDD, pp. 430–439 (2004)Google Scholar
  8. 8.
    Jiang, H., Zhou, S., Guan, J., Zheng, Y.: gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data. In: Li, J., Yang, Q., Tan, A.-H. (eds.) BioDM 2006. LNCS (LNBI), vol. 3916, pp. 48–59. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Kremer, H., Kranen, P., Jansen, T., Seidl, T., Bifet, A., Holmes, G., Pfahringer, B.: An effective evaluation measure for clustering on evolving data streams. In: ACM SIGKDD, pp. 868–876 (2011)Google Scholar
  10. 10.
    Kriegel, H. P., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM TKDD 3(1) (2009)Google Scholar
  11. 11.
    Liao, T.W.: Clustering of time series data - a survey. Pattern Recognition 38(11), 1857–1874 (2005)zbMATHCrossRefGoogle Scholar
  12. 12.
    Minnen, D., Starner, T., Essa, I.A., Isbell, C.: Discovering characteristic actions from on-body sensor data. In: IEEE ISWC, pp. 11–18 (2006)Google Scholar
  13. 13.
    Müller, E., Günnemann, S., Assent, I., Seidl, T.: Evaluating clustering in subspace projections of high dimensional data. PVLDB 2(1), 1270–1281 (2009)Google Scholar
  14. 14.
    Oates, T.: Identifying distinctive subsequences in multivariate time series by clustering. In: ACM SIGKDD, pp. 322–326 (1999)Google Scholar
  15. 15.
    Procopiuc, C.M., Jones, M., Agarwal, P.K., Murali, T.M.: A monte carlo algorithm for fast projective clustering. In: ACM SIGMOD, pp. 418–427 (2002)Google Scholar
  16. 16.
    Sim, K., Aung, Z., Gopalkrishnan, V.: Discovering correlated subspace clusters in 3D continuous-valued data. In: IEEE ICDM, pp. 471–480 (2010)Google Scholar
  17. 17.
    Singhal, A., Seborg, D.: Clustering multivariate time-series data. Journal of Chemometrics 19(8), 427–438 (2005)CrossRefGoogle Scholar
  18. 18.
    Vlachos, M., Gunopulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: IEEE ICDE, pp. 673–684 (2002)Google Scholar
  19. 19.
    Wang, X., Wirth, A., Wang, L.: Structure-based statistical features and multivariate time series clustering. In: IEEE ICDM, pp. 351–360 (2007)Google Scholar
  20. 20.
    Wu, E.H.C., Yu, P.L.H.: Independent Component Analysis for Clustering Multivariate Time Series Data. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 474–482. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: IEEE ICDM, pp. 689–692 (2003)Google Scholar
  22. 22.
    Zhao, L., Zaki, M.J.: TriCluster: An effective algorithm for mining coherent clusters in 3D microarray data. In: ACM SIGMOD, pp. 694–705 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hardy Kremer
    • 1
  • Stephan Günnemann
    • 1
  • Arne Held
    • 1
  • Thomas Seidl
    • 1
  1. 1.RWTH Aachen UniversityGermany

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