Nonlinear component analysis as a kernel eigenvalue problem
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Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
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Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
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Kernel k-means: spectral clustering and normalized cuts
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A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Robust kernel fuzzy clustering
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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
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One-class SVM is a kernel-based method that utilizes the kernel trick for data clustering. However it is only able to detect one cluster of non-convex shape in the input space. In this study, we propose an iterative two-stage one-class SVM to cluster data into several groups. In the first stage, one-class SVM is used to find an optimal weight vector for each cluster in the feature space, while in the second stage the weight vector is used to refine the clustering result. A mechanism is provided to control the optimal hyperplane to work against outliers. Experimental results have shown that our method compares favorably with other kernel based clustering algorithms, such as KKM and KFCM on several synthetic data sets and UCI real data sets.