Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Based Classifers for a Vast Amount of Data
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
A geometrical representation of McCulloch-Pitts neural model and its applications
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Two-stage clustering via neural networks
IEEE Transactions on Neural Networks
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Cluster analysis is one of main methods used in data mining. So far there have existed many cluster analysis approaches such as partitioning method, density-based, k-means, k-nearest neighborhood, etc. Recently, some researchers have explored a few kernel-based clustering methods, e.g., kernel-based K-means clustering. The new algorithms have demonstrated some advantages. So it's needed to explore the basic principle underlain the algorithms such as whether the kernel function transformation can increase the separability of the input data in clustering and how to use the principle to construct new clustering methods. In this paper, we will discuss the problems.