A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Mixtures of probabilistic principal component analyzers
Neural Computation
Faithful representations with topographic maps
Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Adaptive relevance matrices in learning vector quantization
Neural Computation
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
Validity index for clusters of different sizes and densities
Pattern Recognition Letters
Local matrix adaptation in topographic neural maps
Neurocomputing
Divergence-based vector quantization
Neural Computation
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
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We propose relevance learning for unsupervised online vector quantization algorithm based on stochastic gradient descent learning according to the given vector quantization cost function. We consider several widely used models including the neural gas algorithm, the Heskes variant of self-organizing maps and the fuzzy c-means. We apply the relevance learning scheme for divergence based similarity measures between prototypes and data vectors in the vector quantization schemes.