Model selection
Multidimensional similarity structure analysis
Multidimensional similarity structure analysis
Elements of information theory
Elements of information theory
A Bayesian analysis of self-organizing maps
Neural Computation
GTM: the generative topographic mapping
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
ACM Computing Surveys (CSUR)
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Self-Organizing Maps
Constrained Clustering as an Optimization Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ProbMap -- A probabilistic approach for mapping large document collections
Intelligent Data Analysis
An axiomatic approach to soft learning vector quantization and clustering
IEEE Transactions on Neural Networks
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
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
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
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
Local matrix adaptation in topographic neural maps
Neurocomputing
Linear time heuristics for topographic mapping of dissimilarity data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico
Expert Systems with Applications: An International Journal
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Clustering very large dissimilarity data sets
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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In this contribution we present extensions of the Self Organizing Map and clustering methods for the categorization and visualization of data which are described by matrices rather than feature vectors. Rows and Columns of these matrices correspond to objects which may or may not belong to the same set, and the entries in the matrix describe the relationships between them. The clustering task is formulated as an optimization problem: Model complexity is minimized under the constraint, that the error one makes when reconstructing objects from class information is fixed, usually to a small value. The data is then visualized with help of modified Self Organizing Maps methods, i.e. by constructing a neighborhood preserving non-linear projection into a low-dimensional "map-space". Grouping of data objects is done using an improved optimization technique, which combines deterministic annealing with "growing" techniques. Performance of the new methods is evaluated by applying them to two kinds of matrix data: (i) pairwise data, where row and column objects are from the same set and where matrix elements denote dissimilarity values and (ii) co-occurrence data, where row and column objects are from different sets and where the matrix elements describe how often object pairs occur.