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|Title:||Relative entropy fuzzy c-means clustering||Authors:||Zarinbal, M.
Zarandi, Mohammad Hossein Fazel
Türkşen, İsmail Burhan
Relative entropy fuzzy c-means clustering
|Issue Date:||2014||Publisher:||Elsevier Science Inc||Abstract:||Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the performance of such methods might be reduced. Thus, in this paper, a new fuzzy clustering method based on FCM is presented and the relative entropy is added to its objective function as a regularization function to maximize the dissimilarity between clusters. Several examples are provided to examine the performance of the proposed clustering method. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to the observations. (C) 2013 Elsevier Inc. All rights reserved.||URI:||https://doi.org/10.1016/j.ins.2013.11.004
|Appears in Collections:||Endüstri Mühendisliği Bölümü / Department of Industrial Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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