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Open AccessFeature PaperArticle

A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

1
Department of Architecture, Federico II University of Naples, 80134 Naples, Italy
2
Interdepartmental Research Center of Research A. Calza Bini, University of Naples Federico II, 80134 Naples, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(4), 554; https://doi.org/10.3390/electronics9040554
Received: 13 March 2020 / Revised: 22 March 2020 / Accepted: 25 March 2020 / Published: 26 March 2020
(This article belongs to the Special Issue Fuzzy Systems and Data Mining)
One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM. View Full-Text
Keywords: FCM; weighted FCM; fuzzy entropy; fuzzy entropy function FCM; weighted FCM; fuzzy entropy; fuzzy entropy function
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Cardone, B.; Di Martino, F. A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm. Electronics 2020, 9, 554.

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