An intelligent data analysis approach using self-organising-maps (original) (raw)
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Self-Organizing Map and MLP Neural Network -A Practical Use
This material guides you to use Self-Organizing Map (SOM) and MLP Neural Networks (NN) in some practical applications. It was used to introduce NNto some Japanese students. The author thought that it might be useful for the other students so he re-prepared it, for your reference. Because of timelimitation, it may have mistakes. Please tell the author at spiceneuroAT gmail DOT com or http://spiceneuro.wordpress.com If you are interested in NN and SOM, you can use free software, SpiceSOM and SpiceMLP, downloadhere Some data presented here also already in "Data" folder when you setup SpiceSOM and SpiceMLP software. All results here are modeled by SpiceSOMand SpiceMLP.Thank you
The use of artificial neural networks (ANNs) in problems related to water resources has received steadily increasing interest over the last decade or so. The related method of the self-organizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. Consequently, the present paper aims firstly to explain the algorithm and secondly, to review published applications with main emphasis on water resources problems in order to assess how well SOM can be used to solve a particular problem. It is concluded that SOM is a promising technique suitable to investigate, model, and control many types of water resources processes and systems. Unsupervised learning methods have not yet been tested fully in a comprehensive way within, for example water resources engineering. However , over the years, SOM has displayed a steady increase in the number of applications in water resources due to the robustness of the method.
Data Mining and Data Visualization Using Self-Organizing Map (SOM)
2017
Data min ing and data visual izati ons are becoming essential parts in inform ation techno logy in recent e ra. Wi thout the existence of data minin g techno logy, big data can be near imposs ible to be ex tracted and have to be done manually. With the aid of data mining techn ology, now information can be gathered from data sets at much shorter time. The di scovery of data visua lizati ons a lso aid s in managing data into presen table fom) that can be understood by everyone. B ig dimensions can now be reduced to help data be more understandable . In this thesis, Kohonen se lf-organiz ing map(SOM) technique is di scussed and examined for data mining and data visualizations. SOM is a neural network technique that can performs data mining, data classificat ion and data visua lizati ons. SOM Too lbox was used on MATLAB. All steps in SOM are exp la ined in detail s from weight initi a lization until trai nin g is stopped . Graphical explanations of how SOM works are a lso used to help ...
Soil contamination interpretation using self-organizing maps
Issue 1, 2013
The present study deals with the problem of soil contamination risk assessment for a region being actively impact by the routine production of Kremikovtzi metallurgical plant near Sofia, Bulgaria. Although the production is now cancelled, the soil pollution is present and needs careful assessment. The application of self-organizing maps classification strategy of Kohonen makes it possible to identify: a/ pollution sources in the region of interest; b/ spatial patterns of similarity of polluted sites and the reason for the specific pollution.