ACM Transactions on Mathematical Software (TOMS)
Algorithms for clustering data
Algorithms for clustering data
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Theory and Practice of Vector Quantizers Trained on Small Training Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Reasonable properties for the ordering of fuzzy quantities (I)
Fuzzy Sets and Systems
Reasonable properties for the ordering of fuzzy quantities (II)
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Annealed ``Neural Gas'' Network for Robust Vector Quantization
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
On the structure of vector quantizers
IEEE Transactions on Information Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
K-winner machines for pattern classification
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing
Image and Vision Computing
Letters: Soft ranking in clustering
Neurocomputing
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The problem of clustering is often addressed with techniques based on a Voronoi partition of the data space. Vector quantization is based on a similar principle, but it is a different technical problem. We analyze some approaches to the synthesis of a vector quantization codebook, and their similarities with corresponding clustering algorithms. We outline the role of fuzzy concepts in these algorithms, both in data representation and in training. Then we propose an alternative way to use fuzzy concepts as a modeling tool for physical vector quantization systems, Neural Gas with a fuzzy rank function. We apply this method to the problem of quality enhancement in lossy compression and reconstruction of images with vector quantization.