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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
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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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Collaborative fuzzy clustering
Pattern Recognition Letters, 2002Summary: We introduce a new clustering architecture in which several subsets of patterns can be processed together with an objective of finding a structure that is common to all of them. To reveal this structure, the clustering algorithms operating on the separate subsets of data collaborate by exchanging information about local partition matrices.
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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 and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
COPPI, Renato +2 more
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Fuzzy clustering with outliers
PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2002In this paper we introduce a modified objective function for fuzzy clustering. We add an additional weighting factor for each datum and derive necessary conditions for the introduced parameter in order to optimise the objective function. These conditions are used in an alternating optimisation scheme to calculate a partition of sample data.
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Agglomerative Fuzzy Clustering
2016The term fuzzy clustering usually refers to prototype-based methods that optimize an objective function in order to find a (fuzzy) partition of a given data set and are inspired by the classical c-means clustering algorithm. Possible transfers of other classical approaches, particularly hierarchical agglomerative clustering, received much less ...
Christian Borgelt, Rudolf Kruse
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A Combination Scheme for Fuzzy Clustering
International Journal of Pattern Recognition and Artificial Intelligence, 2002In this paper we present a voting scheme for fuzzy cluster algorithms. This voting method allows us to combine several runs of cluster algorithms resulting in a common partition. This helps us to tackle the problem of choosing the appropriate clustering method for a data set where we have no a priori information about it.
Evgenia Dimitriadou +2 more
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A cluster validity index for fuzzy clustering
Information Sciences, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yunjie Zhang +3 more
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A new cluster-validity for fuzzy clustering
Pattern Recognition, 1999Abstract Fuzzy cluster-validity criterion tends to evaluate the quality of fuzzy c-partitions produced by fuzzy clustering algorithms. Many functions have been proposed. Some methods use only the properties of fuzzy membership degrees to evaluate partitions. Others techniques combine the properties of membership degrees and the structure of data.
Noureddine Zahid +2 more
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