Entropy based statistical mechanical fuzzy clustering method and its visualization

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The fuzzy clustering methods which combine the deterministic annealing (DA) approach with fuzzy entropy or Tsallis entropy are examined. By maximizing fuzzy entropy or Tsallis entropy within the framework of the fuzzy c-means (FCM) method, membership functions similar to the statistical mechanical distribution functions are obtained. The characteristics of these entropies and membership functions are investigated from the statistical mechanical point of view.Both fuzzy entropy and Tsallis entropy based FCM are formulated as DA clustering. Numerical experiments showed that these algorithms can divide data more properly and stably than the standard clustering method. The very fast annealing (VFA) method is applied to DA for cooling schedule of the temperature. Also, the membership functions are approximated by linear functions. The obtained results indicate that the proposed method can shorten computational times.Multidimensional questionnaire data were clustered and, by omitting some dimensions, the resultant clusters and memberships were visualized.