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Fuzzy K-Means Clustering With Discriminative Embedding
IEEE Transactions on Knowledge and Data Engineering, 2022Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function.
F. Nie +4 more
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Multigranulation rough-fuzzy clustering based on shadowed sets
Information Sciences, 2020In this study, a new technique of rough-fuzzy clustering based on multigranulation approximation regions is developed to tackle the uncertainty associated with the fuzzifier parameter m.
Jie Zhou +4 more
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The Criterion-Oriented Three-Way Ranking and Clustering Strategies in Fuzzy Decision Environments
IEEE transactions on fuzzy systems, 2022Faced with any decision-making problems with fuzzy multicriteria information, decision-makers generally set a minimum requirement on each criterion for satisfying their own preferences, thereby forming a fuzzy set on the criterion universe, which is ...
Kai Zhang, Jianhua Dai, Zeshui Xu
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Gravitational Fuzzy Clustering
2008Data clustering is an important part of cluster analysis. Numerous clustering algorithms based on various theories have been developed, and new algorithms continue to appear in the literature. In this paper, supposing that each cluster center is a gravity center and each data point has a constant mass, Newton's law of gravity is transformed from m/d2to
Orhan U., Hekim M., Ibrikci T.
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Fuzzy clustering based on feature weights for multivariate time series
Knowledge-Based Systems, 2020As an important set of techniques for data mining, time series clustering methods had been studied by many researchers. Although most existing solutions largely focus on univariate time series clustering, there has been a surge in interest in the ...
Hailin Li, M. Wei
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Reliability-based fuzzy clustering ensemble
Fuzzy Sets Syst., 2020In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not ...
A. Bagherinia +3 more
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High-order Topology for Deep Single-Cell Multiview Fuzzy Clustering
IEEE transactions on fuzzy systemsSingle-cell multiview clustering is essential for analyzing the different cell subtypes of the same cell from different views. Some attempts have been made, but most of these models still struggle to handle single-cell sequencing data, primarily due to ...
Dayu Hu +5 more
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Fuzzy-Based Deep Attributed Graph Clustering
IEEE transactions on fuzzy systemsAttributed graph (AG) clustering is a fundamental, yet challenging, task for studying underlying network structures. Recently, a variety of graph representation learning models has been proposed to effectively infer the node embeddings, which are then ...
Yue Yang +6 more
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Membership Affinity Lasso for Fuzzy Clustering
IEEE transactions on fuzzy systems, 2020Fuzzy clustering generates a membership vector for each data point in the dataset to indicate its belongingness to different clusters. This procedure can be regarded as an encoding process and the obtained vectors of memberships are the new ...
Li Guo +3 more
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Fuzzy Clustering and Fuzzy Co-clustering
2019Fuzzy co-clustering is a fundamental technique for summarizing the structural characteristics of cooccurrence information. In this chapter, following the brief introduction of fuzzy c-Means (FCM) clustering, FCM-induced fuzzy co-clustering model is reviewed with illustrative examples.
Tin-Chih Toly Chen, Katsuhiro Honda
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