José Alfredo Ferreira Costa

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Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover,(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)
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)
The size and complexity of data sets is ever increasing. Clustering, considered the most important unsupervised learning problem, is used to reveal structures and to identify "natural" groupings on the multivariate data. Several competitive learning algorithms were developed for this application. The Growing Neural Gas (GNG) is an incremental algorithm,(More)
This paper presents an approach to the well-known Travelling Salesman Problem (TSP) using Self-Organizing Maps (SOM). The SOM algorithm has interesting topological information about its neurons configuration on cartesian space, which can be used to solve optimization problems. Aspects of initialization, parameters adaptation, and complexity analysis of the(More)
Digital classification methods of remotely sensed images have acquired a growing importance in the automatic recognition of the land cover patterns. The enormous quantity of images that are being generated from an increasing number of highly sophisticated sensor systems require the development of innovative classification methodologies, which allow an(More)