Results 141 to 150 of about 867,994 (193)
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Pattern Recognition Letters, 1995
Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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Fuzziness indices for fuzzy clustering
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.
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IEEE International Conference on Neural Networks, 2002
An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
David B. Fogel, Patrick K. Simpson
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An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
David B. Fogel, Patrick K. Simpson
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IEEE Transactions on Fuzzy Systems, 2015
We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present a particle filter inference technique to estimate the model parameters ...
Taylor C. Glenn +2 more
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We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present a particle filter inference technique to estimate the model parameters ...
Taylor C. Glenn +2 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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Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002
The well-known generalisation of hard c-means (HCM) clustering is fuzzy c-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness. An alternative generalisation of HCM clustering is proposed in this paper.
Dat Tran 0001, Michael Wagner 0004
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The well-known generalisation of hard c-means (HCM) clustering is fuzzy c-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness. An alternative generalisation of HCM clustering is proposed in this paper.
Dat Tran 0001, Michael Wagner 0004
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Fuzzy Sets and Systems, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hideki Kamimura, Masami Kurano
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hideki Kamimura, Masami Kurano
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Dynamic fuzzy clustering using fuzzy cluster loading
International Journal of General Systems, 2006When we obtain clusters through the classification of a given data it is important to interpret the meaning of the obtained clusters. This is particularly true in the clustering of 3-way asymmetric similarity data. This is true because the asymmetric property and the structure of similarity in each cluster are changed over the time periods (or ...
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Proceedings of 3rd IEEE International Conference on Image Processing, 2002
A generalized nonlinear filter called the fuzzy cluster filter is introduced. This filter applies fuzzy clustering inside a running-window to estimate the clean output (i.e., geometrical center of the window). This filter is capable of cancelling the heavy-tailed contaminated Gaussian noise with a good performance.
Mahmood Doroodchi, Ali M. Reza
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A generalized nonlinear filter called the fuzzy cluster filter is introduced. This filter applies fuzzy clustering inside a running-window to estimate the clean output (i.e., geometrical center of the window). This filter is capable of cancelling the heavy-tailed contaminated Gaussian noise with a good performance.
Mahmood Doroodchi, Ali M. Reza
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Pattern Recognition Letters, 1999
Summary: A new algorithm is proposed to carry out fuzzy clustering without making assumptions on initial guesses. The search for good clustering is made by a specific cluster-validity criterion. This tool has been tested on six data sets.
Noureddine Zahid +3 more
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Summary: A new algorithm is proposed to carry out fuzzy clustering without making assumptions on initial guesses. The search for good clustering is made by a specific cluster-validity criterion. This tool has been tested on six data sets.
Noureddine Zahid +3 more
openaire +2 more sources