Self-organizing maps
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Journal of Machine Learning Research
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating and computing density based distance metrics
ICML '05 Proceedings of the 22nd international conference on Machine learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of kernel and spectral methods for clustering
Pattern Recognition
Soft clustering using weighted one-class support vector machines
Pattern Recognition
Locally Constrained Support Vector Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Fast support-based clustering method for large-scale problems
Pattern Recognition
Clustering Stability: An Overview
Foundations and Trends® in Machine Learning
Dynamic Dissimilarity Measure for Support-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
Incremental Support Vector Clustering
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
A Cluster Validity Measure With Outlier Detection for Support Vector Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Multi-local model image set matching based on domain description
Pattern Recognition
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Support Vector Domain Description (SVDD) is an effective method for describing a set of objects. As a basic tool, several application-oriented extensions have been developed, such as support vector clustering (SVC), SVDD-based k-Means (SVDDk-Means) and support vector based algorithm for clustering data streams (SVStream). Despite its significant success, one inherent drawback is that the description is very sensitive to the selection of the trade-off parameter, which is hard to estimate in practice and affects the extensive approaches significantly. To tackle this problem, we propose a novel Position regularized Support Vector Domain Description (PSVDD). In the proposed PSVDD, the complexity of the sphere surface is adaptively regularized by assigning a position-based weighting to each data point, which is computed according to the distance between the corresponding feature space image and the mean of feature space images. To demonstrate the effectiveness of the proposed PSVDD, we apply the position-based weighting to improve two important clustering extensions, i.e., SVC and SVDDk-Means, which respectively result in two new clustering approaches termed PSVC and PSVDDk-Means. Experimental results on several real-world data sets validate the significant improvement achieved by PSVC and PSVDDk-Means.