On the use of self-organizing maps for clustering and visualization

Authors:
Arthur Flexer
Affiliations:
The Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010 Vienna, Austria. E-mail: arthur@ai.univie.ac.at
Venue:
Intelligent Data Analysis
Year:
2001

Citing 5
Cited 14

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Self-organizing maps

Self-organizing maps
GTM: the generative topographic mapping

Neural Computation
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Clustering of the self-organizing map

IEEE Transactions on Neural Networks

TCSOM: Clustering Transactions Using Self-Organizing Map

Neural Processing Letters
Adaptive filtering with the self-organizing map: a performance comparison

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Poisson-Based Self-Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks

Expert Systems with Applications: An International Journal
Anomaly detection in mobile communication networks using the self-organizing map

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Analyzing the Behavior of the SOM through Wavelet Decomposition of Time Series Generated during Its Execution

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A hybrid of sequential rules and collaborative filtering for product recommendation

Information Sciences: an International Journal
Modeling of dynamics using process state projection on the self organizing map

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
On building local models for inverse system identification with vector quantization algorithms

Neurocomputing
An intelligent decision-support model using FSOM and rule extraction for crime prevention

Expert Systems with Applications: An International Journal
Application of visual clustering properties of self organizing map in machine-part cell formation

Applied Soft Computing
Composite algorithm for adaptive mesh construction based on self-organizing maps

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Handling undefined vectors in expensive optimization problems

EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps

Computers & Geosciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization. By reviewing the appropriate literature and theory and own empirical results, we demonstrate that SOMs can be used for clustering or visualization separately, for simultaneous clustering and visualization, and even for clustering via visualization. For all these different kinds of application, SOM is compared to other statistical approaches. This will show SOM to be a flexible tool which can be used for various forms of explorative data analysis but it will also be made obvious that this flexibility comes with a price in terms of impaired performance.