Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Two topographic maps for data visualisation
Data Mining and Knowledge Discovery
Local vs global interactions in clustering algorithms: Advances over K-means
International Journal of Knowledge-based and Intelligent Engineering Systems
Clustering with alternative similarity functions
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Principal Manifolds for Data Visualization and Dimension Reduction
Principal Manifolds for Data Visualization and Dimension Reduction
Clustering with reinforcement learning
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Quantization errors in the harmonic topographic mapping
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
A family of novel clustering algorithms
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Better learning of supervised neural networks based on functional graph: an experimental approach
WSEAS Transactions on Computers
Clustering based adaptive refactoring
WSEAS Transactions on Information Science and Applications
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We [5, 6] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and show that the resulting method is superior to existing methods in that it can be shown to reliably find a globally optimal clustering rather than local optima which other methods often find. We discuss some of the current difficulties with using connectivity graphs for solving clustering problems, and then we introduce a new algorithm to build the connectivity graphs. We compare this new algorithm with some famous algorithms used to build connectivity graphs. The new algorithm is shown to be superior to those in the current literature. We also extend the method to perform topology preserving mappings and show the results of such mappings on artificial and real data.