Titlebook: Advances in Self-Organizing Maps; 8th International Wo Jorma Laaksonen,Timo Honkela Conference proceedings 2011 Springer-Verlag GmbH Berlin

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Growing Graph Network Based on an Online Gaussian Mixture Modelrowing neural networks: no permanent increase in nodes (Gaussian kernels), robustness to noise, and increased speed of constructing networks. This paper presents the theory and algorithm for the proposed method and the results of verification experiments using artificial data.

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Influence of Learning Rates and Neighboring Functions on Self-Organizing Mapsr, inverse-of-time, power series, and heuristics) have been analyzed here. The learning rate has been changed according to epochs and iterations. A comparative analysis has been made with three data sets: glass, wine, and zoo. The quantization error has been measured in order to estimate the SOM quality.

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Mining the City Data: Making Sense of Cities with Self-Organizing MapsAlso, it is important to observe how neighbors geographically close are distributed in terms of the mentioned variables. Self-organizing maps are a tool suitable for planners to seek for those correlations, as we show in our results.

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Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Seriesy crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.