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)


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.


Stochastical clustering overlapping clusters mixture model outlier identification linear regression 


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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

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

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