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Journal of Intelligent & Fuzzy Systems, 2019
In this paper, we propose a new correlation coefficient between intuitionistic fuzzy sets. We then use this new result to compute some examples through which we find that it benefits from such an outcome with some well-known results in the literature. As
N. X. Thao, Mumtaz Ali, F. Smarandache
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In this paper, we propose a new correlation coefficient between intuitionistic fuzzy sets. We then use this new result to compute some examples through which we find that it benefits from such an outcome with some well-known results in the literature. As
N. X. Thao, Mumtaz Ali, F. Smarandache
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FUZZY CLUSTERING BASED ON INTUITIONISTIC FUZZY RELATIONS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004It is well known that an intuitionistic fuzzy relation is a generalization of a fuzzy relation. In fact there are situations where intuitionistic fuzzy relations are more appropriate. This paper discusses the fuzzy clustering based on intuitionistic fuzzy relations. On the basis of max -t & min -s compositions, we discuss an n-step procedure which
Hung, Wen-Liang +2 more
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Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection
Journal of Intelligent & Fuzzy Systems, 2019In this paper, a hybrid fuzzy clustering techniques using Salp Swarm Algorithm (SSA) is proposed. The proposed fuzzy clustering method is used to optimize the cluster centroids obtained as an under sampling method.
S. Majhi +3 more
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Fuzzy clustering: Determining the number of clusters
2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), 2012In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering.
Řezanková, H., Húsek, D. (Dušan)
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IEEE transactions on fuzzy systems, 2019
Multiview and multiexemplar fuzzy clustering aims at effectively integrating the fuzzy membership matrix of each individual view to search for a final partition of objects in which each cluster may well be represented by one and even multiple exemplars ...
Yuanpeng Zhang, F. Chung, Shitong Wang
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Multiview and multiexemplar fuzzy clustering aims at effectively integrating the fuzzy membership matrix of each individual view to search for a final partition of objects in which each cluster may well be represented by one and even multiple exemplars ...
Yuanpeng Zhang, F. Chung, Shitong Wang
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Integrated Rough Fuzzy Clustering for Categorical data Analysis
Fuzzy Sets Syst., 2019In recent times, advanced data mining research has been mostly focusing on clustering of categorical data, where a natural ordering in attribute values is missing.
Indrajit Saha +2 more
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Fuzzy clustering with outliers
PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2002In this paper we introduce a modified objective function for fuzzy clustering. We add an additional weighting factor for each datum and derive necessary conditions for the introduced parameter in order to optimise the objective function. These conditions are used in an alternating optimisation scheme to calculate a partition of sample data.
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IEEE International Conference on Neural Networks, 2002
An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
D.B. Fogel, P.K. Simpson
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An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
D.B. Fogel, P.K. Simpson
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Pattern Recognition Letters, 1995
Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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A comparison of fuzzy clustering algorithms for bearing fault diagnosis
Journal of Intelligent & Fuzzy Systems, 2018Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues.
Chuan Li +6 more
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