Results 21 to 30 of about 22,177,297 (251)
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.
Braak, C.J.F., ter +3 more
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Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms
Advances in Fuzzy Systems, 2015 Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM)
Arindam Chaudhuri
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A Federated Fuzzy c-means Clustering Algorithm
Proceedings of WILF 2021, the 13th International Workshop on Fuzzy Logic and Applications (WILF 2021), 2021 http://ceur-ws.org/Vol-3074/paper08 ...
Bárcena, José Luis Corcuera +4 more
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Evolving single- and multi-model fuzzy classifiers with FLEXFIS-class [PDF]
, 2007 [2] R. Santos, E. Dougherty, and J. A. Jaakko, “Creating fuzzy rules for image classification using biased data clustering,” in SPIE proceedings series (SPIE proc. ser.) International Society for Optical Engineering proceedings series.
Angelov, Plamen +5 more
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Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation [PDF]
, 2003 This paper identifies a generic axiom framework for prioritised fuzzy constraint satisfaction problems (PFCSPs), and proposes methods to instantiate it (i.e., to construct specific schemes which obey the generic axiom framework).
Luo, X. +14 more
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On Fuzzy c-Means for Data with Tolerance
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2006 This paper presents two new clustering algorithms which are based on the entropy regularized fuzzyc-means and can treat data with some errors. First, the tolerance is formulated and introduce into optimization problems of clustering. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of
Ryuichi Murata +3 more
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Fuzzy clustering of time series gene expression data with cubic-spline [PDF]
, 2013 Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
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IEEE Access, 2020 Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious.
Yuan Huang +4 more
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An improved fuzzy clustering image segmentation algorithm combining spatial information
Xi'an Gongcheng Daxue xuebao, 2021 In order to improve the ability of fuzzy C-means (FCM) clustering algorithm to suppress noise, an improved fuzzy clustering image segmentation algorithm was proposed.
Xudong LIU +4 more
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Comparative Analysis of Fuzzy C Means and Fuzzy C Means++
IITM Journal of Management and IT, 2018 Cluster analysis is one of the most useful means for identifying relations and patterns in the area of data mining. It can be defined as partitioning of large volumes of data into various clusters that share some property or attribute. The most common clustering algorithm is k means.
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