Competitive learning algorithms for vector quantization
Neural Networks
Characterization and detection of noise in clustering
Pattern Recognition Letters
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Topology representing networks
Neural Networks
Neural Networks
Application of the least trimmed squares technique to prototype-based clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Self-Organizing Maps
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Neural-Gas for Function Approximation: A Heuristic for Minimizing the Local Estimation Error
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
Towards a robust fuzzy clustering
Fuzzy Sets and Systems - Data analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Robust clustering by pruning outliers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Pattern Recognition
Vector quantization based approximate spectral clustering of large datasets
Pattern Recognition
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In practical cluster analysis tasks, an efficient clustering algorithm should be less sensitive to parameter configurations and tolerate the existence of outliers. Based on the neural gas (NG) network framework, we propose an efficient prototype-based clustering (PBC) algorithm called enhanced neural gas (ENG) network. Several problems associated with the traditional PBC algorithms and original NG algorithm such as sensitivity to initialization, sensitivity to input sequence ordering and the adverse influence from outliers can be effectively tackled in our new scheme. In addition, our new algorithm can establish the topology relationships among the prototypes and all topology-wise badly located prototypes can be relocated to represent more meaningful regions. Experimental resultson synthetic and UCI datasets show that our algorithm possesses superior performance in comparison to several PBC algorithms and their improved variants, such as hard c-means, fuzzy c-means, NG, fuzzy possibilistic c-means, credibilistic fuzzy c-means, hard/fuzzy robust clustering and alternative hard/fuzzy c-means, in static data clustering tasks with a fixed number of prototypes.