Márcio Luiz De Andrade Netto

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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)
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 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)
An implementation of a computational tool to generate new summaries from new source texts in Portuguese language, by means of connectionist approach (artificial neural networks) is presented. Among other contributions that this work intends to bring to natural language processing research, the employment of more biologically plausible connectionist(More)
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