Advertisement

Clustering and Outlier Identification: Fixed Point Cluster Analysis

  • C. Hennig
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Fixed Point Cluster Analysis (FPCA) is introduced in this paper. FPCA is a new method for non-hierarchical cluster analysis. It is related to outlier identification. Its aim is to find groups of points generated by a common stochastic model without assuming a global model for the whole dataset. FPCA allows for points not belonging to any cluster, for the existence of clusters with a different shape, and for overlapping clusters. FPCA is applicated to the clustering of p—dimensional metrical data, 0-1-vectors, and linear regression data.

Keywords

Stochastical clustering overlapping clusters mixture model outlier identification linear regression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Davies, P. L. and Gather, U. (1993). The identification of multiple outliers, Journal of the American Statistical Association 88, 782–801.CrossRefGoogle Scholar
  2. Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J. and Ostrowski, E. (1994). A Handbook of Small Datasets, Chapman & Hill, London.Google Scholar
  3. Hennig, C. (1997). Datenanalyse mit Modellen für Cluster linearer Regression, dissertation, Universität Hamburg.Google Scholar
  4. Rousseeuw, P. J. and Leroy, A. M. (1987). Robust Regression and Outlier Detection, Wiley, New York.CrossRefGoogle Scholar
  5. Titterington, D. M., Smith, A. F. M. and Makov, U. E. (1985). Statistical Analysis of Finite Mixture Distributions, Wiley, New York.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • C. Hennig
    • 1
  1. 1.Institut für Mathematische StochastikUniversität HamburgGermany

Personalised recommendations