A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms


  • Subhagata Chattopadhyay
  • Dilip Kumar Pratihar
  • Sanjib Chandra De Sarkar


Fuzzy clustering, fuzzy c-means algorithm, entropy-based algorithms, self-organizing maps


Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-C-means (FCM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Moreover, in FCM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. The cluster centers and membership values of the data points with them are updated through some iterations. On the other hand, entropy-based fuzzy clustering (EFC) algorithm works based on a similarity-threshold value. Contrary to FCM, in EFC, the cluster centers are real, that is, they are chosen from the data points. In the present paper, the performances of these algorithms have been compared on four data sets, such as IRIS, WINES, OLITOS and psychosis (collected with the help of forty doctors), in terms of the quality of the clusters (that is, discrepancy factor, compactness, distinctness) obtained and their computational time. Moreover, the best set of clusters has been mapped into 2-D for visualization using a self-organizing map (SOM).


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Author Biographies

Subhagata Chattopadhyay

School of Computer Studies
Department of Computer Science and Engineering
National Institute of Science and Technology
Berhampur 761008
Orissa, India

Dilip Kumar Pratihar

Department of Mechanical Engineering
Indian Institute of Technology
Kharagpur 721302
West Bengal, India

Sanjib Chandra De Sarkar

School of Electrical Sciences
Indian Institute of Technology Bhubaneswar
Bhubaneswar 751013
Orissa, India




How to Cite

Chattopadhyay, S., Pratihar, D. K., & Sarkar, S. C. D. (2012). A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms. COMPUTING AND INFORMATICS, 30(4), 701–720. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/191