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Diverse fuzzy c-means for image clustering
Pattern Recognition Letters, 2020Abstract Image clustering is a key technique for better accomplishing image annotation and searching in large image repositories. Fuzzy c-means and its variations have achieved excellent performance on image clustering because they allow each image to belong to more than one cluster.
Lingling Zhang 0005 +4 more
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A Novel Fuzzy C-Means Clustering Algorithm
2006This paper proposes a novel fuzzy c-means clustering algorithm which treats attributes differently. Moreover, by analyzing the Hessian Matrix of the new algorithm's objective function, we get a rule of parameters' selection.
Cui-Xia Li, Jian Yu 0001
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The credibilistic fuzzy c means clustering algorithm
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2002Since the introduction of fuzzy clustering by Ruspini (1970), fuzzy logic has provided a family of interesting clustering algorithms which expanded the abilities of 'crisp' techniques. The most popular among these algorithms is the fuzzy c means algorithm (FCM). However, FCM and most of its variants are sensitive to the presence of outliers in the data
Krishna Kant Chintalapudi, Moshe Kam
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Cluster Validity for the Fuzzy c-Means Clustering Algorithrm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers,
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Robust weighted fuzzy c-means clustering
2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008Nowadays, the fuzzy c-means method (FCM) became one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM).
Amir Hossein Hadjahmadi +2 more
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On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems, 1995Many functionals have been proposed for validation of partitions of object data produced by the fuzzy c-means (FCM) clustering algorithm. We examine the role a subtle but important parameter-the weighting exponent m of the FCM model-plays in determining the validity of FCM partitions. The functionals considered are the partition coefficient and entropy
Nikhil R. Pal, James C. Bezdek
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On semi-supervised fuzzy c-means clustering
2009 IEEE International Conference on Fuzzy Systems, 2009We have two methods of pattern classification, one is supervised and the other is unsupervised. Unsupervised classification, which is called clustering and classifies data except external criteria, is very useful in the methods of pattern classification so that it has been applied in many fields.
Yasunori Endo +3 more
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Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values.
Richard J. Hathaway, James C. Bezdek
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A fuzzy C-means clustering placement algorithm
1993 IEEE International Symposium on Circuits and Systems, 2002A placement algorithm based on fuzzy c-means clustering techniques is presented. The first stage of the algorithm is to construct the max-product transitive closure of the connection matrix, which is then used as a fuzzy similarity relation in a clustering process.
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A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'URSO, Pierpaolo, GIORDANI, Paolo
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