Clustering the Linearly Inseparable Clusters

  • Hamid Reza Shahdoosti
  • Omid Khayat
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)


In this paper a new method for clustering is introduced that works fine for linearly inseparable clusters. This algorithm uses the of patterns’ concentration specification. We consider each pattern as a heat source to determine concentration by heat effect. Because of high concentration in clusters, heat in the clusters is higher than out of them, and this fact is used to determine and bound the clusters. CLIC algorithm can be implemented for any linearly inseparable cluster. The classification rate of this algorithm is higher than previously known methods (k-means, fuzzy c-means, etc). Finally, we cluster real iris data by using CLIC algorithm.


Arbitrary Point Classification Rate Heat Function Iris Data Main Maximum 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Hamid Reza Shahdoosti
    • 1
  • Omid Khayat
    • 1
  1. 1.Department of Biomedical EngineeringAmirkabir UniversityIran

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