Data mining using rule extraction from Kohonen self-organising maps
نویسندگان
چکیده
منابع مشابه
Visualisation of gait data with Kohonen self-organising neural maps.
Self-organising artificial neural networks were used to reduce the complexity of joint kinematic and kinetic data, which form part of a typical instrumented gait assessment. Three-dimensional joint angles, moments and powers during the gait cycle were projected from the multi-dimensional data space onto a topological neural map, which thereby identified gait stem-patterns. Patients were positio...
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The Kohonen self-organizing feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two dimensional form. This feature is used for classification and clustering of data. However, a great deal of e...
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Exploratory data mining using artificial neural networks offers an alternative dimension to data mining, in particular techniques geared towards data clustering and classification. In this paper, we argue the case for using neural networks as a viable data mining tool that can provide statistical insights and models from large data-sets. We demonstrate how Self-Organizing Kohonen Maps, an unsup...
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ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2005
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-005-0002-1