An algorithm for drawing general undirected graphs
Information Processing Letters
Neural Networks
Self-Organizing Maps
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Local Variance Driven Self-Organization for Unsupervised Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Neurocomputing
Unsupervised recursive sequence processing
Neurocomputing
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Gamma-filter self-organizing neural networks for time series analysis
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In order to focus on frequent sequence patterns an entropy maximization strategy is utilized which controls the creation of new neurons. Experimental results demonstrate reduced time complexity compared to MNG while retaining similar accuracy in time series representation.