Yasuda, M. (2016) Approximate Determination of Q-Parameter for FCM with Tsallis Entropy Maximization. Proceedings of the Joint 8th International Conference on Soft Computing and 17th International Symposium on Advanced Intelligent Systems, 700-705.
https://doi.org/10.1109/scis-isis.2016.0151

has been cited by the following article:

• TITLE: On Utilization of K-Means for Determination of q-Parameter for Tsallis-Entropy-Maximized-FCM

  AUTHORS: Makoto Yasuda

  KEYWORDS: Fuzzy c-Means, K-Means, Tsallis Entropy, Entropy Maximization, Entropy Regularization, Deterministic Annealing

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.10 No.7, June 23, 2017

  ABSTRACT: In this paper, we consider a fuzzy c-means (FCM) clustering algorithm combined with the deterministic annealing method and the Tsallis entropy maximization. The Tsallis entropy is a q-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of FCM, membership functions similar to statistical mechanical distribution functions can be derived. One of the major considerations when using this method is how to determine appropriate q values and the highest annealing temperature, Thigh, for a given data set. Accordingly, in this paper, a method for determining these values simultaneously without introducing any additional parameters is presented. In our approach, the membership function is approximated by a series of expansion methods and the K-means clustering algorithm is utilized as a preprocessing step to estimate a radius of each data distribution. The results of experiments indicate that the proposed method is effective and both q and Thigh can be determined automatically and algebraically from a given data set.