Journal
ELECTRONICS
Volume 9, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/electronics9040554
Keywords
FCM; weighted FCM; fuzzy entropy; fuzzy entropy function
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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.
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