Kernel k’-means Algorithm for Clustering Analysis

  • Yue Zhao
  • Shuyi Zhang
  • Jinwen Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


k’-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and penalizing competitive learning mechanism into the k-means paradigm such that the number of clusters can be automatically determined for a given dataset. This paper further proposes the kernelized versions of k’-means algorithms with four different discrepancy metrics. It is demonstrated by the experiments on both synthetic and real-world datasets that these kernel k’-means algorithms can automatically detect the number of actual clusters in a dataset, with a classification accuracy rate being considerably better than those of the corresponding k’-means algorithms.


Clustering analysis k-means algorithm Mercer kernels Kernel method 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yue Zhao
    • 1
  • Shuyi Zhang
    • 1
  • Jinwen Ma
    • 1
  1. 1.Department of Information Science, School of Mathematical Sciences And LMAMPeking UniversityBeijingChina

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