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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)

Abstract

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.

Keywords

Arbitrary Point Classification Rate Heat Function Iris Data Main Maximum 
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.

References

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    Wolkenhauer Olaf Fuzzy classification, the iris-and-admission data setsGoogle Scholar

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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