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A New Approach for Partitional Clustering Using Entropy Notation and Hopfield Network

  • Vahid Abrishami
  • Maryam Sabzevari
  • Mahdi Yaghobi
Conference paper

Abstract

This paper proposes a new clustering algorithm which employs an improved stochastic competitive Hopfield network in order to organize data patterns into natural groups, or clusters, in an unsupervised manner. This Hopfield network uses an entropy based energy function to overcome the problem of insufficient understanding of the data and to obtain the optimal parameters for clustering. Additionally, a chaotic variable is introduced in order to escape from the local minima and gain a better clustering. By minimizing the entropy of each cluster using Hopfield network, we achieve a superior accuracy to that of the best existing algorithms such as optimal competitive Hopfield model, stochastic optimal competitive Hopfield network, k-means and genetic algorithm. The experimental results demonstrate the scalability and robustness of our algorithm over large datasets.

Keywords

Data Item Cluster Problem Quadratic Assignment Problem Partitional Cluster Maximum Clique Problem 
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 London Limited 2011

Authors and Affiliations

  1. 1.Young Researchers Club (YRC), Islamic Azad UniversityMashhad BranchIran
  2. 2.Islamic Azad UniversityMashhad BranchIran

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