Algorithms for clustering data
Algorithms for clustering data
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
Handbook of pattern recognition and image processing (vol. 2): computer vision
Handbook of pattern recognition and image processing (vol. 2): computer vision
Convergence and ordering of Kohonen's batch map
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Self-Organizing neural networks: recent advances and applications
Self-Organizing neural networks: recent advances and applications
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Clustering Algorithms
Visual Explorations in Finance
Visual Explorations in Finance
Kohonen Maps
Self-Organizing Maps
Self-Organizing Map Formation: Foundations of Neural Computation
Self-Organizing Map Formation: Foundations of Neural Computation
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Self organization of a massive document collection
IEEE Transactions on Neural Networks
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
Numerical Learning Method for Process Neural Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Essentials of the self-organizing map
Neural Networks
| Hi-index | 0.00 |
The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual data bases. The SOM is related to the classical vector quantization (VQ), which is used extensively in digital signal processing and transmission. Like in VQ, the SOM represents a distribution of input data items using a finite set of models. In the SOM, however, these models are automatically associated with the nodes of a regular (usually two-dimensional) grid in an ordered fashion such that more similar models become automatically associated with nodes that are adjacent in the grid, whereas less similar models are situated farther away from each other in the grid. This organization, a kind of similarity diagram of the models, makes it possible to obtain an insight into the topographic relationships of data, especially of high-dimensional data items. If the data items belong to certain predetermined classes, the models (and the nodes) can be calibrated according to these classes. An unknown input item is then classified according to that node, the model of which is most similar with it in some metric used in the construction of the SOM. A new finding introduced in this paper is that an input item can even more accurately be represented by a linear mixture of a few best-matching models. This becomes possible by a least-squares fitting procedure where the coefficients in the linear mixture of models are constrained to nonnegative values.