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Projected fuzzy C-means with probabilistic neighbors
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jikui +5 more
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From Soft Clustering to Hard Clustering: A Collaborative Annealing Fuzzy $c$-Means Algorithm
IEEE transactions on fuzzy systemsThe fuzzy c-means clustering algorithm is the most widely used soft clustering algorithm. In contrast to hard clustering, the cluster membership of data generated using the fuzzy c-means algorithm is ambiguous.
Hongzong Li, Jun Wang
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An Efficient Federated Multiview Fuzzy C-Means Clustering Method
IEEE transactions on fuzzy systemsMultiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges
Xingchen Hu +5 more
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Unconstrained Fuzzy C-Means Algorithm
IEEE Transactions on Pattern Analysis and Machine IntelligenceFuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks.
Feiping Nie +3 more
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2007 IEEE International Fuzzy Systems Conference, 2007
Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any ...
Prodip Hore +2 more
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Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any ...
Prodip Hore +2 more
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Pattern Recognition Letters, 1996
A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced. The method reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable. The usefulness of the algorithm is exemplified in the problems
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A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced. The method reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable. The usefulness of the algorithm is exemplified in the problems
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Effective Fuzzy Possibilistic C-Means
Proceedings of the ASE BigData & SocialInformatics 2015, 2015Using clustering analysis for identifying cancer types in Colon cancer dataset is extremely difficult task because of high-dimensionality gene with noise. Most of the existing clustering methods for colon to achieve types of cancers often hamper the interpretability of the structure.
S. Ramathilagam, S. R. Kannan, R. Devi
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Fuzzy C-Means clustering through SSIM and patch for image segmentation
Applied Soft Computing, 2020In this study, we propose a new robust Fuzzy C-Means (FCM) algorithm for image segmentation called the patch-based fuzzy local similarity c-means (PFLSCM).
Yiming Tang, Fuji Ren, W. Pedrycz
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Applied Soft Computing, 2020
The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment images. However, the FCM method is not robustness and less accurate for noise images.
Qingsheng Wang +3 more
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The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment images. However, the FCM method is not robustness and less accurate for noise images.
Qingsheng Wang +3 more
semanticscholar +1 more source
2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019
Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 more
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Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 more
openaire +1 more source