Topology representing networks
Neural Networks
Self-organizing maps
Convergence and ordering of Kohonen's batch map
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
ACM Computing Surveys (CSUR)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A unified framework for model-based clustering
The Journal of Machine Learning Research
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
SOM-based algorithms for qualitative variables
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Online algorithm for the self-organizing map of symbol strings
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Magnification control for batch neural gas
Neurocomputing
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Patch Relational Neural Gas --- Clustering of Huge Dissimilarity Datasets
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Matrix Learning for Topographic Neural Maps
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Patch clustering for massive data sets
Neurocomputing
Median Topographic Maps for Biomedical Data Sets
Similarity-Based Clustering
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
Line Image Classification by NG×SOM: Application to Handwritten Character Recognition
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
The Complexity of the Batch Neural Gas Extended to Local PCA
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Clustering Bacteria Species Using Neural Gas: Preliminary Study
Computational Intelligence Methods for Bioinformatics and Biostatistics
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Computing with words with the ontological self-organizing map
IEEE Transactions on Fuzzy Systems - Special section on computing with words
A principal components analysis neural gas algorithm for anomalies clustering
WSEAS TRANSACTIONS on SYSTEMS
Topographic mapping of large dissimilarity data sets
Neural Computation
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Probabilistic self-organizing maps for qualitative data
Neural Networks
Divergence based online learning in vector quantization
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Local matrix adaptation in topographic neural maps
Neurocomputing
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Linear time heuristics for topographic mapping of dissimilarity data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Prototype-based classification of dissimilarity data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Global coordination based on matrix neural gas for dynamic texture synthesis
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
The mathematics of divergence based online learning in vector quantization
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Clustering very large dissimilarity data sets
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Approximation techniques for clustering dissimilarity data
Neurocomputing
Kernel robust soft learning vector quantization
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Batch neural gas with deterministic initialization for color quantization
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Detection of locally relevant variables using SOM-NG algorithm
Engineering Applications of Artificial Intelligence
Semi-supervised clustering of large data sets with kernel methods
Pattern Recognition Letters
Learning vector quantization for (dis-)similarities
Neurocomputing
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Neural Gas (NG) constitutes a very robust clustering algorithm given Euclidean data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.