Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Self-Organizing Maps
The Journal of Machine Learning Research
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Neural Gas Algorithms with Application to Cluster Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel-based subtractive clustering method
Pattern Recognition Letters
Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
New clustering methods for interval data
Computational Statistics
A survey of kernel and spectral methods for clustering
Pattern Recognition
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Clustering via kernel decomposition
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Fuzzy C-Means in High Dimensional Spaces
International Journal of Fuzzy System Applications
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This paper presents variable-wise kernel fuzzy c-means clustering methods in which dissimilarity measures are obtained as sums of Euclidean distances between patterns and centroids computed individually for each variable by means of kernel functions. The advantage of the proposed approach over the conventional kernel clustering methods is that it allows us to use adaptive distances which change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. This kind of dissimilarity measure is suitable to learn the weights of the variables during the clustering process, improving the performance of the algorithms. Another advantage of this approach is that it allows the introduction of various fuzzy partition and cluster interpretation tools. Experiments with synthetic and benchmark datasets show the usefulness of the proposed algorithms and the merit of the fuzzy partition and cluster interpretation tools.