On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
On the ordering conditions for self-organizing maps
Neural Computation
Self-organizing maps
Ordering of self-organizing maps in multidimensional cases
Neural Computation
Neural maps and topographic vector quantization
Neural Networks
Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison
Neural Processing Letters
Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
Magnification Control in Self-Organizing Maps and Neural Gas
Neural Computation
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Unsupervised recursive sequence processing
Neurocomputing
Clustering of time series data-a survey
Pattern Recognition
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Discriminative clustering for market segmentation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Letters: Clustering of the Self-Organizing Time Map
Neurocomputing
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A method for clustering time-varying data by using neural networks, i.e. Kohonen self-organizing maps (SOMs), is suggested. Some dissimilarity measures for capturing the temporal structure of the data are introduced and used in Kohonen SOMs allowing clustering of temporal data. Another method for clustering time-varying data, called dynamic tandem analysis (DTA), based on the sequential utilization of dynamic factor analysis and cluster analysis, is also considered. The methods are applied to telecommunications market segmentation on real data. The obtained results are compared and discussed.