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Fuzzy c-means for Fuzzy Hierarchical Clustering
The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., 2005This paper describes an algorithm for building fuzzy hierarchies. These are hierarchies where the elements can have fuzzy membership to the nodes. The paper presents an approach that mainly follows a bottom-up strategy, and describes the functions needed to operate with fuzzy variables.
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Projected fuzzy C-means with probabilistic neighbors
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jikui Wang +5 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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Generalized fuzzy c-means algorithms
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2000This paper proposes generalized fuzzy c-means (FCM) algorithms. The clustering problem is formulated as a constrained minimization problem, whose solution depends on the selection of a constraint function that satisfies certain conditions. If the constraint function is proportional to the generalized mean of the membership values, the solution of this ...
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Complex fuzzy c-means algorithm
Artificial Intelligence Review, 2011In this paper a new clustering algorithm is presented: A complex-based Fuzzy c-means (CFCM) algorithm. While the Fuzzy c-means uses a real vector as a prototype characterizing a cluster, the CFCM's prototype is generalized to be a complex vector (complex center). CFCM uses a new real distance measure which is derived from a complex one. CFCM's formulas
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A fuzzy clustering model of data and fuzzy c-means
Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002The multiple prototype fuzzy clustering model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identified.
Susana Nascimento +2 more
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Relative entropy fuzzy c-means clustering
Information Sciences, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marzie Zarinbal +2 more
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Fuzzy C-Means Stereo Segmentation
2015An extension to the popular fuzzy c-means clustering method is proposed by introducing an additional disparity cue. The creation of the fuzzy clusters is driven by a degree of the stereo match and thus it enables to separate the objects not only by their different colours but also on their different spatial depth.
Michal Krumnikl, Eduard Sojka, Jan Gaura
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Rough C-means and Fuzzy Rough C-means for Colour Quantisation
Fundamenta Informaticae, 2012Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best ...
Gerald Schaefer +4 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 0001 +3 more
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