Advertisement

An Effective Performance of Fuzzy Hierarchical Clustering Using Time Series Data Streams

  • V.Kavitha
  • M.Punithavalli
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

Mining Time Series data has a remarkable development of concentration in today’s humanity. Clustering time series is a trouble that has applications in a wide variety of fields and has recently fascinated a huge amount of researchers. Time series data are frequently large and may surround outliers. In addition, time series are a special type of data set where elements have a chronological ordering. Therefore clustering of such data stream is an important issue in the data mining process. The clustering algorithms and its effectiveness on various applications are compared to extend an innovative method to solve the existing problem. This paper presents a new method called Fuzzy Hierarchical clustering algorithm is implemented. Experimental results on ECG data evaluate the processing qualities of the system is more accurate than the previous system.

Keywords

Time Series Data Streams Hierarchical Clustering Fuzzy Logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dingi, H., Trajcevski, G., Scheuestern, P., Wang, X., Keogh, E.: Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures. ACM Proceedings of the VLDB Endowment 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  2. 2.
    Talbot, L.M., Talbot, B.G., Peterson, R.E., Tolley, H.D., Mecham, H.D.: Application of fuzzy grade-of membership clustering to analysis of remote sensing data. Journal on Climate 12, 200–219 (1999)CrossRefGoogle Scholar
  3. 3.
    Rodriguess, P.P., Pedroso, J.P.: Hierarchical Clustering of Time Series Data Streams. IEEE Transactions on Knowledge and Data Engineering 20(5), 615–627 (2008)CrossRefGoogle Scholar
  4. 4.
    Hautamaki, V., Nykanen, P., Franti, P.: Time Series Clustering by Approximate Prototypes. In: IEEE 2008 (2008)Google Scholar
  5. 5.
    Bagnall, A.J., Janacek, G.J.: Clustering time series from ARMA models with Clipped data. In: ACM Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 49–58 (2004)Google Scholar
  6. 6.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R.: Clustering Data Streams: Theory and practice. IEEE Transactions on Knowledge and Data Engineering 15(3), 515–528 (2003)CrossRefGoogle Scholar
  7. 7.
    Yin, J., Zhou, D., Xie, Q.-Q.: A Clustering Algorithm for Time series Data. In: Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2006), pp. 119–122 (2006)Google Scholar
  8. 8.
    Sato, M., Sato, Y.: Fuzzy clustering model for fuzzy data. IEEE Transactions on Fuzzy Systems 4, 2123–2128 (1995)zbMATHGoogle Scholar
  9. 9.
    Rodrigues, P.P., Gama, J.: A semi Fuzzy Approach for Online Divisive Agglomerative Clustering. In: EPIA 2007 Proceeding of the Artificial Intelligence (2007) ISBN : 3-540-77000-3 978-3-540-77000-8Google Scholar
  10. 10.
    Sato, M., Sato, Y.: Fuzzy clustering model for fuzzy data. IEEE transactions on Fuzzy Systems 4, 2123–2128 (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • V.Kavitha
    • 1
  • M.Punithavalli
    • 2
  1. 1.Dept of Computer ScienceKarpagam UniversityCoimbatoreIndia
  2. 2.Dr.SNS Rajalakshmi College of Arts and ScienceCoimbatoreIndia

Personalised recommendations