Feature Selection in Unsupervised Context: Clustering Based Approach

  • Artur Klepaczko
  • Andrzej Materka
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)


In this paper we present a novel feature selection method that is applicable in unsupervised learning tasks. The method is based on clustering quality measures, which reflect different aspects of clustering performance. Sequential Floating Forward Search algorithm is employed to search through the original feature space for the best possible subset. Main stress has been put on the objectivism of the new technique, so that it could be applied in various classification tasks. Results of experiments with texture images are presented in order to confirm effectiveness of the method.


Feature Selection Linear Discriminant Analysis Feature Subset Texture Image Feature Selection Method 
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 Berlin Heidelberg 2005

Authors and Affiliations

  • Artur Klepaczko
    • 1
  • Andrzej Materka
    • 1
  1. 1.Technical University of LodzLodzPoland

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