Why Does Subsequence Time-Series Clustering Produce Sine Waves?

  • Tsuyoshi Idé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

The data mining and machine learning communities were surprised when Keogh et al. (2003) pointed out that the k-means cluster centers in subsequence time-series clustering become sinusoidal pseudo-patterns for almost all kinds of input time-series data. Understanding this mechanism is an important open problem in data mining. Our new theoretical approach (based on spectral clustering and translational symmetry) explains why the cluster centers of k-means naturally tend to form sinusoidal patterns.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, J.: Making subsequence time series clustering meaningful. In: Proc. IEEE Intl. Conf. Data Mining, pp. 114–121 (2005)Google Scholar
  2. 2.
    Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means, spectral clustering and normalized cuts. In: Proc. Tenth ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining, pp. 551–556 (2004)Google Scholar
  3. 3.
    Ding, C., He, X.: K-means clustering via principal component analysis. In: Proc. Intl. Conf. Machine Learning, pp. 225–232 (2004)Google Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Chichester (2000)Google Scholar
  5. 5.
    Idé, T.: Pairwise symmetry decomposition method for generalized covariance analysis. In: Proc. IEEE Intl. Conf. Data Mining, pp. 657–660 (2005)Google Scholar
  6. 6.
    Keogh, E.: Data mining and machine learning in time series databases. In: Tutorial Notes of the Tenth ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining (2004)Google Scholar
  7. 7.
    Keogh, E., Folias, T.: The UCR time series data mining archive (2002), http://www.cs.ucr.edu/~eamonn/TSDMA/index.html
  8. 8.
    Keogh, E., Lin, J., Truppel, W.: Clustering of time series subsequences is meaningless: Implications for previous and future research. In: Proc. IEEE Intl. Conf. Data Mining, pp. 115–122. IEEE, Los Alamitos (2003)Google Scholar
  9. 9.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856 (2001)Google Scholar
  10. 10.
    Zha, H., Ding, C., Gu, M., He, X., Simon, H.: Spectral relaxation for k-means clustering. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1057–1064 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tsuyoshi Idé
    • 1
  1. 1.Tokyo Research LaboratoryIBM ResearchKanagawaJapan

Personalised recommendations