Márcio Luiz De Andrade Netto

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Clustering is the process of discovering groups within the data, based on similarities, with a minimal, if any, knowledge of their structure. The self-organizing (or Kohonen) map (SOM) is one of the best known neural network algorithms. It has been widely studied as a software tool for visualization of high-dimensional data. Important features include(More)
This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. The data clustering is made using the self-organizing (or Kohonen) map (SOM). Different partitions of the trained SOM are obtained from different segmentations of the U-matrix(More)
Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each(More)
This paper presents the principal results of a detailed study about the use of the meaningful fractal fuzzy dimension measure in the problem in determining adequately the topological dimension of output space of a self-organizing map. This fractal measure is conceived by combining the fractals theory and fuzzy approximate reasoning. In this work this(More)
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