Titlebook: Advances in Self-Organising Maps; Nigel Allinson,Hujun Yin,Jon Slack Conference proceedings 2001 Springer-Verlag London Limited 2001 Data

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A Supervised Self-Organizing Map for Structured Data,e, we suggest a new version of SOM, using the supervised learning approach. We compare the supervised version and the unsupervised version of SOM-SD on a benchmark problem involving visual patterns. As may be expected, the supervised version is able to solve the classification problem using very com

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Exploring Financial Crises Data with Self-Organizing Maps (SOM),plore the multidimensional properties of a data set of recent speculative attacks in search of potential associations between economic descriptors, and the size and duration of speculative attack’s real effects. We found that speculative attack’s real effects were associated with: the health of the

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Analysing Health Inequalities Using SOM,to identify health inequality structure is critical. It is believed that health inequality analysis is a complex problem. Self- organisation mapping is therefore employed in this paper to analyse health inequalities based on a data set provided by the Centre for Disease Control and Prevention, USA (

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Integrating Contextual Information into Text Document Clustering with Self-Organizing Maps,rs. The topographic map approaches usually use the common vector space model for text document representation. We present here a new two stage representation which uses sentences as intermediate information units. In this way contextual information is preserved and influences the process of self-org

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Recursive learning rules for SOMs,ious possibilities regarding the norm and the direction of the adaptation vectors. The performance and convergence of each rule is evaluated by two criteria: topology preservation and quantization error.

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An approach to automated interpretation of SOM,h higher level object consists of a varying number of lower level objects. Both low and high level data is available and needs to be utilized. The information from lower levels is transferred to higher level using data histograms of lower level clusters. The clusters are formed and interpreted autom

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