Self-organizing maps
ACM Computing Surveys (CSUR)
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
The Journal of Machine Learning Research
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks
Expert Systems with Applications: An International Journal
Translation Invariance in the Polynomial Kernel Space and Its Applications in kNN Classification
Neural Processing Letters
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Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering. The algorithm compares better with popular clustering algorithms, namely K-Means, Neural Gas, Self Organizing Maps, on a synthetic dataset and three UCI real data benchmarks, IRIS data, Wisconsin breast cancer database, Spam database.