Algorithms for clustering data
Algorithms for clustering data
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
Kernel PCA as a visualization tools for clusters identifications
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Clustering interval data through kernel-induced feature space
Journal of Intelligent Information Systems
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Many clustering algorithms require some parameters that often are neither a priori known nor easy to estimate, like the number of classes. Measures of clustering quality can consequently be used to a posteriori estimate these values. This paper proposes such an index of clustering evaluation that deals with kernel methods like kernel-k-means. More precisely, it presents an extension of the well-known Davies & Bouldin's index. Kernel clustering methods are particularly relevant because of their ability to deal with initially non-linearly separable clusters. The interest of the following clustering evaluation is then to get around the issue of the not explicitly known data transformation of such kernel methods. Kernel Davies & Bouldin's index is finally used to a posteriori estimate the parameters of the kernel-k-means method applied on some toys datasets and Fisher's Iris dataset.