The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm

  • Jinwen Ma
  • Bin Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.


Cost Function Mean Square Error Weight Vector Mahalanobis Distance Gradient Descent Algorithm 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jinwen Ma
    • 1
  • Bin Cao
    • 1
  1. 1.Department of Information Science, School of Mathematical Sciences and LMAMPeking UniversityBeijingChina

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