Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
GTM: the generative topographic mapping
Neural Computation
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
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)
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Patch clustering for massive data sets
Neurocomputing
Topographic mapping of large dissimilarity data sets
Neural Computation
Divergence-based vector quantization
Neural Computation
IEEE Transactions on Information Theory
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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
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Recently, diverse high quality prototype-based clustering techniques have been developed which can directly deal with data sets given by general pairwise dissimilarities rather than standard Euclidean vectors. Examples include affinity propagation, relational neural gas, or relational generative topographic mapping. Corresponding to the size of the dissimilarity matrix, these techniques scale quadratically with the size of the training set, such that training becomes prohibitive for large data volumes. In this contribution, we investigate two different linear time approximation techniques, patch processing and the Nystrom approximation. We apply these approximations to several representative clustering techniques for dissimilarities, where possible, and compare the results for diverse data sets.