Continuous Trend-Based Clustering in Data Streams

  • Maria Kontaki
  • Apostolos N. Papadopoulos
  • Yannis Manolopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5182)


Trend analysis of time series is an important problem since trend identification enables the prediction of the near future. In streaming time series the problem is more challenging due to the dynamic nature of the data. In this paper, we propose a method to continuously clustering a number of streaming time series based on their trend characteristics. Each streaming time series is transformed to a vector by means of the Piecewise Linear Approximation (PLA) technique. The PLA vector comprises pairs of values (timestamp, trend) denoting the starting time of the trend and the type of the trend (either UP or DOWN) respectively. A distance metric for PLA vectors is introduced. We propose split and merge criteria to continuously update the clustering information. Moreover, the proposed method handles outliers. Performance evaluation results, based on real-life and synthetic data sets, show the efficiency and scalability of the proposed scheme.


Data Stream Piecewise Linear Approximation Split Criterion Silhouette Coefficient Performance Evaluation Result 
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.
    Charikar, M., O’Callaghan, L., Panigrahy, R.: Better Streaming Algorithms for Clustering Problems. In: Proceedings of STOC, pp. 30–39 (2003)Google Scholar
  2. 2.
    Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows. In: Proceedings of ACM-SIAM SODA, pp. 635–644 (2002)Google Scholar
  3. 3.
    Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceedings of ACM SIGKDD, pp. 71–80 (2000)Google Scholar
  4. 4.
    Fung, G.P.C., Yu, J.X., Lam, W.: News Sensitive Stock Trend Prediction. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 481–493. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R., OCallaghan, L.: Clustering Data Streams: Theory and Practice. IEEE TKDE 15(3), 515–528 (2003)Google Scholar
  6. 6.
    Hulten, G., Spencer, L., Domingos, P.: Mining Time Changing Data Streams. In: Proceedings of ACM KDD, pp. 97–106 (2001)Google Scholar
  7. 7.
    Hutson, J.K.: TRIX - Triple Exponential Smoothing Oscillator. Technical Analysis of Stocks and Commodities, 105–108 (July/August 1983)Google Scholar
  8. 8.
    Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y.: Continuous Trend-Based Classification of Streaming Time Series. In: Eder, J., Haav, H.-M., Kalja, A., Penjam, J. (eds.) ADBIS 2005. LNCS, vol. 3631, pp. 294–308. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Sacchi, L., Bellazzi, R., Larizza, C., Magni, P., Curk, T., Petrovic, U., Zupan, B.: Clustering and Classifying Gene Expressions Data through Temporal Abstractions. In: Proceedings of IDAMAP, Protaras, Cyprus (2003)Google Scholar
  10. 10.
    Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream Monitoring Under the Time Warping Distance. In: Proceedings of ICDE, Istanbul, Turkey, pp. 1046–1055 (2007)Google Scholar
  11. 11.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2006)Google Scholar
  12. 12.
    Yoon, J.P., Luo, Y., Nam, J.: A Bitmap Approach to Trend Clustering for Prediction in Time-Series Databases. In: Proceedings of Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Florida, USA (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Maria Kontaki
    • 1
  • Apostolos N. Papadopoulos
    • 1
  • Yannis Manolopoulos
    • 1
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

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