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In this work we propose to use fuzzy prototypes for classification tasks. We create the prototypes by first clusterizing, separately, the available data for each class. Then we create a fuzzy set around each class center, according to clusterization indices that take into account both local and global data. We also investigate the use of an index to guide the process of obtaining a suitable number of clusters for posterior classification. We illustrate the approach using a simple example and show some results for some well-known data sets.
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