Clustering and Outlier Identification: Fixed Point Cluster Analysis
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.
KeywordsStochastical clustering overlapping clusters mixture model outlier identification linear regression
Unable to display preview. Download preview PDF.
- 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
- Hennig, C. (1997). Datenanalyse mit Modellen für Cluster linearer Regression, dissertation, Universität Hamburg.Google Scholar
- Titterington, D. M., Smith, A. F. M. and Makov, U. E. (1985). Statistical Analysis of Finite Mixture Distributions, Wiley, New York.Google Scholar