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
Approximation schemes for Euclidean k-medians and related problems
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A stochastic self-organizing map for proximity data
Neural Computation
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Self-Organizing Maps
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A constant-factor approximation algorithm for the k-median problem
Journal of Computer and System Sciences - STOC 1999
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
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
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
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
The use of a supervised k-means algorithm on real-valued data with applications in health
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Fast and exact out-of-core and distributed k-means clustering
Knowledge and Information Systems
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Speeding up the dissimilarity self-organizing maps by branch and bound
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Fuzzy labeled self-organizing map with label-adjusted prototypes
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis.