Estimating the number of clusters from distributional results of partitioning a given data set

  • U. Möller


When estimating the optimal value of the number of clusters, C, of a given data set, one typically uses, for each candidate value of C, a single (final) result of the clustering algorithm. If distributional data of size T are used, these data come from Tdata sets obtained, e.g., by a bootstrapping technique. Here a new approach is introduced that utilizes distributional data generated by clustering the original data T times in the framework of cost function optimization and cluster validity indices. Results of this method are reported for model data (100 realizations) and gene expression data. The probability of correctly estimating the number of clusters was often higher compared to recently published results of several classical methods and a new statistical approach (Clest).


Cluster Algorithm Validity Index Cluster Validity Index Cluster Trial Cost Function Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • U. Möller
    • 1
  1. 1.Bioinformatics — Pattern Recognition GroupHans Knoll Institute for Natural Products Research JenaGermany

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