Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Soft Computing and Human-Centered Machines
Soft Computing and Human-Centered Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Kernel clustering-based discriminant analysis
Pattern Recognition
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Expert Systems with Applications: An International Journal
A Kernel-Based Two-Stage One-Class Support Vector Machines Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Possibilistic Clustering in Feature Space
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Membership enhancement with exponential fuzzy clustering for collaborative filtering
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A kernel prototype-based clustering algorithm
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
3D head model classification using KCDA
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Effective fuzzy c-means clustering algorithms for data clustering problems
Expert Systems with Applications: An International Journal
Two novel fuzzy clustering methods for solving data clustering problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The fuzzy c-means (FCM) is sensitive to noise or outliers because this method has the probabilistic constraint that the memberships of a data point across classes sum to one. To solve the problem, a possibilistic c-means clustering (PCM) has been proposed by Krishnapuram and Keller. An advantage of PCM is highly robust in a noisy environment. On the other hand, some clustering algorithms using the kernel trick, e.g., kernel-based FCM and kernel-based LVQ clustering, have been studied to obtain nonlinear classification boundaries. In this paper, an entropy-based possibilistic c-means clustering using the kernel trick has been proposed as more robust method. Numerical examples are shown and effect of the kernel method is discussed.