Results 11 to 20 of about 172,977 (352)
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 Ants and Clustering [PDF]
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2007 A swarm-intelligence-inspired approach to clustering data is described. The algorithm consists of two stages. In the first stage of the algorithm, ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated using a reformulated fuzzy C-means (FCM) criterion. In the second stage, the best cluster centers found are
Parag M. Kanade, Lawrence O. Hall
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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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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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Multimodal decision-level fusion for person authentication [PDF]
, 1999 In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization ...
Bors, A.G., Chatzis, V., Pitas, I.
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RESAMPLING FOR FUZZY CLUSTERING [PDF]
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2007 Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster ...
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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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Frontiers in Psychology, 2014 Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous.
Jocelyn H Bolin +3 more
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