A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation
IEEE Access, 2020 This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization
Licheng Jiao +4 more
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Fuzzy Cluster Analysis: Pseudometrics and Fuzzy Clusters
Cybernetics and Computer Technologies, 2023 Introduction. Clustering problems arise in various spheres of human activity. In cases where there are no initial data sufficient for statistical analysis or information obtained from experts is used, fuzzy models are proposed that take into account different types of uncertainty and more argumentatively reflect real situations that model systems of ...
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Possibilistic and fuzzy clustering methods for robust analysis of non-precise data [PDF]
, 2017 This work focuses on robust clustering of data affected by imprecision. The imprecision is managed in terms of fuzzy sets. The clustering process is based on the fuzzy and possibilistic approaches.
Ferraro, MARIA BRIGIDA, Giordani, Paolo
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Comparison of different strategies of utilizing fuzzy clustering in structure identification [PDF]
, 2007 Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification.
Kılıç, Kemal +2 more
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A Framework of Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data
JOIV: International Journal on Informatics Visualization, 2021 Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters.
Iwan Tri Riyadi Yanto +3 more
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International Journal of Computational Intelligence Systems, 2020 In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale ...
Zhenyu Lu, Xianyun Huang
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Automatic Fuzzy Clustering Framework for Image Segmentation
IEEE transactions on fuzzy systems, 2020 Clustering algorithms by minimizing an objective function share a clear drawback of having to set the number of clusters manually. Although density peak clustering is able to find the number of clusters, it suffers from memory overflow when it is used ...
Tao Lei +5 more
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Unsupervised EA-Based Fuzzy Clustering for Image Segmentation
IEEE Access, 2020 This paper presents an unsupervised fuzzy clustering based on evolutionary algorithm for image segmentation. It needs no prior information about exact numbers of segments.
Mengxuan Zhang +4 more
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Statistical and fuzzy clustering methods and their application to clustering provinces of Iraq based on agricultural products [PDF]
AUT Journal of Mathematics and Computing, 2020 The important approaches to statistical and fuzzy clustering are reviewed and compared, and their applications to an agricultural problem based on a real-world data are investigated.
Israa Atiyah, Seyed Mahmoud Taheri
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Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]
, 2009 Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Bink, M.C.A.M. +3 more
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