A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams

  • Jessica Lin
  • Michai Vlachos
  • Eamonn Keogh
  • Dimitrios Gunopulos
  • Jianwei Liu
  • Shoujian Yu
  • Jiajin Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)

Abstract

In streaming time series the Clustering problem is more complex, since the dynamic nature of streaming data makes previous clustering methods inappropriate. In this paper, we propose firstly a new method to evaluate Clustering in streaming time series databases. First, we introduce a novel multi-resolution PAA (MPAA) transform to achieve our iterative clustering algorithm. The method is based on the use of a multi-resolution piecewise aggregate approximation representation, which is used to extract features of time series. Then, we propose our iterative clustering approach for streaming time series. We take advantage of the multiresolution property of MPPA and equip a stopping criteria based on Hoeffding bound in order to achieve fast response time. Our streaming time-series clustering algorithm also works by leveraging off the nearest neighbors of the incoming streaming time series datasets and fulfill incremental clustering approach. The comprehensive experiments based on several publicly available real data sets shows that significant performance improvement is achieved and produce high-quality clusters in comparison to the previous methods.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lin, J., Vlachos, M., Keogh, E., Gunopulos, D.: Iterative Incremental Clustering of Time Series. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 14–18. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Huhtala, Y., Kärkkäinen, J., Toivonen, H.: Mining for Similarities in Aligned Time Series Using Wavelets. In: Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Orlando, Florida. SPIE Proceedings Series, vol. 3695, pp. 150–160 (1999)Google Scholar
  3. 3.
    Struzik, Z., Siebes, A.: The Haar Wavelet Transform in The Time Series Similarity Paradigm. In: Proc 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 12–22 (1999)Google Scholar
  4. 4.
    Carney, D., Cetinternel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.: Monitoring streams: A New Class of Data Management Applications. In: Proc. 28th Int. Conf. on Very Large Data Bases, pp. 215–226 (2002)Google Scholar
  5. 5.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Journal of Knowledge and Information Systems 3(3), 263–286 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 13-30 (1963)Google Scholar
  7. 7.
    Yi, B., Faloutsos, C.: Fast Time Sequence Indexing for Arbitrary Lp Norms. In: Proceedings of the 26th Int’l Conference on Very Large Databases, Cairo, Egypt, September 10-14, pp. 385–394; l Database Management, Berlin, Germany, July 26-28, pp. 55–68 (2000)Google Scholar
  8. 8.
    Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, Boston, MA, pp. 71–80. ACM Press, New York (2000)CrossRefGoogle Scholar
  9. 9.
    Gama, J., Medas, P., Rodrigues, P.: Concept Drift in Decision-Tree Learning for Data Streams. In: Proceedings of the Fourth European Symposium on Intelligent Technologies and their implementation on Smart Adaptive Systems, Aachen, Germany, Verlag Mainz, pp. 218–225 (2004)Google Scholar
  10. 10.
    Ralaivola, L., dAlche-Buc, F.: Incremental Support Vector Machine Learning: A Local Approach. In: Proceedings of the Annual Conference of the European Neural Network Society, pp. 322–329 (2001)Google Scholar
  11. 11.
    Bay, S.D.: The UCI KDD Archive. Department of Information and Computer Science. University of California, Irvine (1999), http://kdd.ics.uci.edu Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jessica Lin
    • 1
  • Michai Vlachos
    • 1
  • Eamonn Keogh
    • 1
  • Dimitrios Gunopulos
    • 1
  • Jianwei Liu
    • 2
  • Shoujian Yu
    • 2
  • Jiajin Le
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of California, RiversideRiverside
  2. 2.College of Computer Science & TechnologyDonghua University 

Personalised recommendations