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G-SteX: Greedy Stem Extension for Free-Length Constrained Motif Discovery

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

Abstract

Most available motif discovery algorithms in real-valued time series find approximately recurring patterns of a known length without any prior information about their locations or shapes. In this paper, a new motif discovery algorithm is proposed that has the advantage of requiring no upper limit on the motif length. The proposed algorithm can discover multiple motifs of multiple lengths at once, and can achieve a better accuracy-speed balance compared with a recently proposed motif discovery algorithm. We then briefly report two successful applications of the proposed algorithm to gesture discovery and robot motion pattern discovery.

Keywords

Motif Discovery Motif Length Motif Detection Motif Occurrence Motif Discovery Algorithm 
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.
    Catalano, J., Armstrong, T., Oates, T.: Discovering Patterns in Real-Valued Time Series. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 462–469. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD 2003, pp. 493–498 (2003)Google Scholar
  3. 3.
  4. 4.
    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
  5. 5.
    Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: ICDM, p. 8 (November 2005)Google Scholar
  6. 6.
    Li, Y., Lin, J.: Approximate variable-length time series motif discovery using grammar inference. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, p. 10. ACM (2010)Google Scholar
  7. 7.
    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
  8. 8.
    Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15(2), 107–144 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Minnen, D., Starner, T., Essa, I., Isbell, C.: Improving activity discovery with automatic neighborhood estimation. In: Int. Joint Conf. on Artificial Intelligence (2007)Google Scholar
  11. 11.
    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 2004, vol. 3, pp. 2203–2208 (2004)Google Scholar
  12. 12.
    Mohammad, Y., Nishida, T.: Learning interaction protocols using augmented baysian networks applied to guided navigation. In: IROS 2010, pp. 4119–4126 (2010)Google Scholar
  13. 13.
    Mohammad, Y., Nishida, T.: Constrained motif discovery in time series. New Generation Computing 27(4), 319–346 (2009)zbMATHCrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Mohammad, Y., Nishida, T.: On comparing SSA-based change point discovery algorithms. In: 2011 IEEE/SICE International Symposium on System Integration (2011) (to appear)Google Scholar
  16. 16.
    Mohammad, Y., Nishida, T., Okada, S.: Unsupervised simultaneous learning of gestures, actions and their associations for human-robot interaction. In: Proceedings of the 2009 IEEE IROS, pp. 2537–2544 (2009)Google Scholar
  17. 17.
    Mueen, A., Keogh, E., Zhu, Q., Cash, S.: Exact discovery of time series motifs. In: Proc. of 2009 SIAM (2009)Google Scholar
  18. 18.
    Nevill-Manning, C.G., Witten, I.H.: Identifying Hierarchical Structure in Sequences: A linear-time algorithm. Journal of Artificial Intelligence Research 7(1), 67–82 (1997)zbMATHGoogle Scholar
  19. 19.
    Nunthanid, P., Niennattrakul, V., Ratanamahatana, C.: Discovery of variable length time series motif. In: ECTI-CON 2011, pp. 472–475 (2011)Google Scholar
  20. 20.
    Oates, T.: PERUSE: An unsupervised algorithm for finding recurring patterns in time series. In: ICDM, pp. 330–337 (2002)Google Scholar
  21. 21.
    Tang, H., Liao, S.S.: Discovering original motifs with different lengths from time series. Know.-Based Syst. 21(7), 666–671 (2008)CrossRefGoogle Scholar

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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