CPMD: A Matlab Toolbox for Change Point and Constrained Motif Discovery

  • Yasser Mohammad
  • Yoshimasa Ohmoto
  • Toyoaki Nishida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)

Abstract

Change Point Discovery (CPD) and Constrained Motif Discovery (CMD) are two essential problems in data mining with applications in many fields including robotics, economics, neuroscience and other fields. In this paper, we show that these two problems are related and report the development of a MATLAB Toolbox (CPMD) that encapsulates several useful algorithms including new variants to solve these two related problems. The Toolbox is then used to study the effect of distance function choice in CPD.

Keywords

Distance Function Change Point Optional Parameter Motif Discovery Singular Spectrum Analysis 
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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References

  1. 1.
    Alippi, C., Roveri, M.: An adaptive CUSUM-based test for signal change detection. In: 2006 IEEE International Symposium on Circuits and Systems, p. 4 (2006)Google Scholar
  2. 2.
    Basseville, M., Nikiforov, I.: Detection of abrupt changes: theory and application, vol. 15. Citeseer (1993)Google Scholar
  3. 3.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD, pp. 493–498 (2003)Google Scholar
  4. 4.
    Gombay, E.: Change detection in autoregressive time series. J. Multivar. Anal. 99(3), 451–464 (2008)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Hirano, S., Tsumoto, S.: Mining similar temporal patterns in long time-series data and its application to medicine. In: ICDM, p. 219 (2002)Google Scholar
  6. 6.
    Ide, T., Inoue, K.: Knowledge discovery from heterogeneous dynamic systems using change-point correlations. In: Proc. SIAM Intl. Conf. Data Mining (2005)Google Scholar
  7. 7.
    Jensen, K.L., Styczynxki, M.P., Rigoutsos, I., Stephanopoulos, G.N.: A generic motif discovery algorithm for sequenctial data. BioInformatics 22(1), 21–28 (2006)CrossRefGoogle Scholar
  8. 8.
    Kadambe, S., Boudreaux-Bartels, G.: Application of the wavelet transform for pitch detection of speech signals. IEEE Transactions on Information Theory 38(2), 917–924 (1992)CrossRefGoogle Scholar
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: The 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International, pp. 53–68 (2002)Google Scholar
  10. 10.
    Minnen, D., Essa, I., Isbell, C.L., Starner, T.: Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery. In: ICDM (2007)Google Scholar
  11. 11.
    Minnen, D., Starner, T., Essa, I., Isbell, C.: Improving activity discovery with automatic neighborhood estimation. In: Int. Joint Conf. on Arti. Intell. (2007)Google Scholar
  12. 12.
    Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takanobu, H., Rocella, S., Carrozza, M., Dario, P., Takanishi, A.: Effective emotional expressions with expression humanoid robot we-4rii: integration of humanoid robot hand rch-1. In: IROS, vol. 3, pp. 2203–2208 (2004)Google Scholar
  13. 13.
    Mohammad, Y., Nishida, T.: Learning interaction protocols using augmented baysian networks applied to guided navigation. In: IROS, pp. 4119–4126 (2010)Google Scholar
  14. 14.
    Mohammad, Y., Nishida, T.: Constrained motif discovery in time series. New Generation Computing 27(4), 319–346 (2009)MATHCrossRefGoogle Scholar
  15. 15.
    Mohammad, Y., Nishida, T.: Robust Singular Spectrum Transform. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 123–132. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Mohammad, Y., Nishida, T.: Using physiological signals to detect natural interactive behavior. Applied Intelligence 33, 79–92 (2010)CrossRefGoogle Scholar
  17. 17.
    Mohammad, Y., Nishida, T.: Discovering causal change relationships between processes in complex systems. In: 2011 IEEE/SICE SII (2011) (to appear)Google Scholar
  18. 18.
    Mohammad, Y., Nishida, T.: On comparing SSA-based change point discovery algorithms. In: 2011 IEEE/SICE SII (2011) (to appear)Google Scholar
  19. 19.
    Mohammad, Y., Nishida, T.: Self-initiated imitation learningdiscovering what to imitate. In: AAMAS (submitted, 2012)Google Scholar
  20. 20.
    Mohammad, Y., Nishida, T., Okada, S.: Unsupervised simultaneous learning of gestures, actions and their associations for human-robot interaction. In: IROS, pp. 2537–2544 (2009)Google Scholar
  21. 21.
    Moskvina, V., Zhigljavsky, A.: An algorithm based on singular spectrum analysis for change-point detection. Communications in Statistics, Simulation and Computation 32(4), 319–352 (2003)MathSciNetMATHGoogle Scholar
  22. 22.
    Oates, T.: PERUSE: An unsupervised algorithm for finding recurring patterns in time series. In: ICDM, pp. 330–337 (2002)Google Scholar
  23. 23.
    Page, E.S.: Continuous inspection schemes. Biometrika 44, 100–115 (1954)MathSciNetGoogle Scholar
  24. 24.
    Tang, H., Liao, S.S.: Discovering original motifs with different lengths from time series. Know.-Based Syst. 21(7), 666–671 (2008)CrossRefGoogle Scholar
  25. 25.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yasser Mohammad
    • 1
  • Yoshimasa Ohmoto
    • 2
  • Toyoaki Nishida
    • 2
  1. 1.Assiut UniversityEgypt
  2. 2.Kyoto UniversityJapan

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