Advertisement

Fuzzy c-Means Clustering in the Presence of Noise Cluster for Time Series Analysis

  • Arnold C. Alanzado
  • Sadaaki Miyamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)

Abstract

Cluster analysis for time series is becoming increasingly important in many real applications. Clustering plays an important role in extracting information from the noise in economic and financial time series. In this paper we consider the use of fuzzy c-means clustering method in the context of econometric analysis of time-series data. We discuss and demonstrate a methodology for model identification and estimation that is based on the fuzzy c-means algorithm in the presence of noise cluster that is widely used in the context of pattern recognition. The effect of noise on time-series prediction is important to quantify for accurate forecasting. The noise clustering approach is based on the concept of first defining a noise cluster and then defining a similarity or dissimilarity measure for the noise cluster.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chatfield, C.: An Introduction the Analysis of Time Series, 5th edn. Chapman and Hall, London (1996)zbMATHGoogle Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  3. 3.
    Davé, R.N., Krishnapuram, R.: Robust clustering methods: a unified view. IEEE Trans. Fuzzy Syst. 5(2), 270–293 (1997)CrossRefGoogle Scholar
  4. 4.
    Ruspini, E.H.: A New Approach to Clustering. Information Control 15, 22–32 (1969)zbMATHCrossRefGoogle Scholar
  5. 5.
    Dunn, J.C.: Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well- Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Barnet, V., Lewis, T.: Outliers in statistical data, 3rd edn. John Wiley, Chichester (1994)Google Scholar
  7. 7.
    Davé, R.N.: Characterization and detection of noise in clustering. Pattern Recognition Letters 12, 657–664 (1991)CrossRefGoogle Scholar
  8. 8.
    Miyagishi, K., Yasutomi, Y., Ichihashi, H., Honda, K.: Fuzzy Clustering with regularization by K-L information. In: 16th Fuzzy System Symposium, Akita, September 6-8, pp. 549–550 (2000) (in Japanese)Google Scholar
  9. 9.
    Miyamoto, S., Alanzado, A.C.: Fuzzy C-Means and Mixture Distribution Models in the Presence of Noise Clusters. International Journal of Image and Graphics 2(4), 573–586 (2002)CrossRefGoogle Scholar
  10. 10.
    Kaiser, R., Maravall, A.: Seasonal outliers in time series, Banco de Espana - Servicio de Estudios paper number 9915 (1999) Google Scholar
  11. 11.
    Barnett, V., Lewis, T.: Outliers in Statistical Data. Wiley, Chichester (1978)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Arnold C. Alanzado
    • 1
  • Sadaaki Miyamoto
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan
  2. 2.Department of Risk Engineering, School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan

Personalised recommendations