Topology representing networks
Neural Networks
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A stochastic self-organizing map for proximity data
Neural Computation
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Self-Organizing Maps
Clustering Algorithms
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Partitioning with Indefinite Kernels Using the Nyström Extension
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Annealed ``Neural Gas'' Network for Robust Vector Quantization
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Clustering in massive data sets
Handbook of massive data sets
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified framework for model-based clustering
The Journal of Machine Learning Research
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Neural Gas Algorithms with Application to Cluster Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Recursive self-organizing network models
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
Approximate data mining in very large relational data
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
On the information and representation of non-Euclidean pairwise data
Pattern Recognition
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Magnification control for batch neural gas
Neurocomputing
A tutorial on spectral clustering
Statistics and Computing
Patch clustering for massive data sets
Neurocomputing
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Competitive learning algorithms for robust vector quantization
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Relational generative topographic mapping
Neurocomputing
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Accelerating kernel neural gas
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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
Relational extensions of learning vector quantization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Approximation techniques for clustering dissimilarity data
Neurocomputing
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Patch processing for relational learning vector quantization
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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
Data analysis of (non-)metric proximities at linear costs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
Learning vector quantization for (dis-)similarities
Neurocomputing
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Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature encoding of information. Often data are available in the form of pairwise distances only, such as arise from a kernel matrix, a graph, or some general dissimilarity measure. In such cases, NG and SOM cannot be applied directly. In this article, we introduce relational topographic maps as an extension of relational clustering algorithms, which offer prototype-based representations of dissimilarity data, to incorporate neighborhood structure. These methods are equivalent to the standard (vectorial) techniques if a Euclidean embedding exists, while preventing the need to explicitly compute such an embedding. Extending these techniques for the general case of non-Euclidean dissimilarities makes possible an interpretation of relational clustering as clustering in pseudo-Euclidean space. We compare the methods to well-known clustering methods for proximity data based on deterministic annealing and discuss how far convergence can be guaranteed in the general case. Relational clustering is quadratic in the number of data points, which makes the algorithms infeasible for huge data sets. We propose an approximate patch version of relational clustering that runs in linear time. The effectiveness of the methods is demonstrated in a number of examples.