A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two soft relatives of learning vector quantization
Neural Networks
Self-organizing maps
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
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Some Theoretical Aspects of the Neural Gas Vector Quantizer
Similarity-Based Clustering
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Validity index for clusters of different sizes and densities
Pattern Recognition Letters
Divergence-based vector quantization
Neural Computation
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
A note on self-organizing semantic maps
IEEE Transactions on Neural Networks
Clustering by fuzzy neural gas and evaluation of fuzzy clusters
Computational Intelligence and Neuroscience
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In this paper we propose the combination of fuzzy c-means for clustering with neighborhood cooperativeness from the neural gas vector quantizer. The new approach avoids the sensitivity of fuzzy c-means with respect to initialization as it is known from neural gas compared to crisp c-means. Thereby, the neural gas paradigm of neighborhood offers a greater flexibility than those of the self-organizing map, which was combined with fuzzy c-means before. However, a careful reformulation of neighborhood has to be done to keep the validity of the convergence proof of this previous approach. We demonstrate the properties for an artificial as well as for real world data.