Algorithms for clustering data
Algorithms for clustering data
Data mining: concepts and techniques
Data mining: concepts and techniques
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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
Clustering evaluation in feature space
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Kernel PCA has been proven to be a powerful technique as a nonlinear feature extractor and a pre-processing step for classification algorithms. KPCA can also be considered as a visualization tool; by looking at the scatter plot of the projected data, we can distinguish the different clusters within the original data. We propose to use visualization given by KPCA in order to decide the number of clusters. K-means clustering algorithm on both data and projected space is then applied using synthetic and real datasets. The number of clusters discovered by the user is compared to the Davies-Bouldin index originally used as a way of deciding the number of clusters.