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Autonomous and Deterministic Probabilistic Neural Network Using Global k-Means

  • Roy Kwang Yang Chang
  • Chu Kiong Loo
  • Machavaram V. C. Rao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Normally, user have to define the number of clusters through trial and error method, which makes random initialization to be of stochastic nature. Global k-means was chosen as the initialization method because it can autonomously find the number of clusters using a selection criterion and can provide deterministic clustering results. The proposed algorithm was tested on benchmark datasets and real world data from the cooling water system in a power plant.

Keywords

Probabilistic Neural Network Cluster Centroid Correct Classification Rate Dimension Size Random Initialization 
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 2006

Authors and Affiliations

  • Roy Kwang Yang Chang
    • 1
  • Chu Kiong Loo
    • 2
  • Machavaram V. C. Rao
    • 2
  1. 1.Faculty of Information Science and TechnologyMutilmedia UniversityMelakaMalaysia
  2. 2.Faculty of Engineering TechnologyMutilmedia UniversityMelakaMalaysia

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