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Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Neural Networks
Topology representing networks
Neural Networks
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
GTM: the generative topographic mapping
Neural Computation
A Hierarchical Self-Organizing Map Model for Sequence Recognition
Neural Processing Letters
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Self-Organizing Maps
Clustering based on conditional distributions in an auxiliary space
Neural Computation
A Recurrent Self-Organizing Map for Temporal Sequence Processing
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
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
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
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
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The self-organizing map (SOM) is a valuable tool for data visualization and data mining for potentially high-dimensional data of an a priori fixed dimensionality. We investigate SOMs for sequences and propose the SOM-S architecture for sequential data. Sequences of potentially infinite length are recursively processed by integrating the currently presented item and the recent map activation, as proposed in the SOMSD presented in (IEEE Trans. Neural Networks 14(3) (2003) 491). We combine that approach with the hyperbolic neighborhood of Ritter (Proceedings of PKDD-01, Springer, Berlin, 2001, pp. 338-349), in order to account for the representation of possibly exponentially increasing sequence diversification over time. Discrete and real-valued sequences can be processed efficiently with this method, as we will show in experiments. Temporal dependencies can be reliably extracted from a trained SOM. U-matrix methods, adapted to sequence processing SOMs, allow the detection of clusters also for real-valued sequence elements.