Uma interpretação nebulosa dos mapas de Kohonen

Abstract

The Data Mining techniques, based on the Kohonen self-organizing maps have been largely used for classifying signals in several areas of expertise. Generally, the SOM network (Self- Organizing Maps) is used to specify similarity relationships between objects by adopting cluster analysis. The computational cost, data preparation and mathematical modeling can influence the interpretation of results, in which those from the evaluation classes are among its limitations. The Kohonen maps do not permit detailed evaluation of the class of objects, which may however be defined by the class limits, in other words defining a measure that can link when an object belonging to a particular class can migrate from one class to another . To adopt this approach the solutions proposed in this Masters dissertation are designed to implement the Kohonen self-organizing maps and the fuzzy logic to generate neighborhoods between classes aimed at applying these techniques on a two-case study for classifying signals from potencial power systems and Biomedical output signals adopting an interpretation of the Kohonen nebula maps. The work is basically divided into three stages: the first which would be followed by a review of the data-mining techniques and fuzzy logic shown in literature; the second focuses on applying the classifier algorithm using artificial neural networks, specifically the usage of neural networks as SOM data mining techniques to enable the classification of signals while the third step demonstrates the SOM network fuzzy logic multidisciplinary approach as an alternative tool of the data-mining methods.