Guillermo Sánchez-Díaz

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In the developed countries, especially over the last decade, there has been an explosive growth in the capability to generate, collect and use very large data sets. The objects of these data sets could be simultaneously described by quantitative and qualitative attributes. At present, algorithms able to process either very large data sets (in metric spaces)(More)
In this paper, we expose the possibilities of the Logical Combinatorial Pattern Recognition tools for Data Mining from (Very) Large Mixed Incomplete Data Sets. Starting from the real existence of a lot of complex structured (very) large data sets, our Laboratories are working in the application of the methods, the techniques and in general, the philosophy(More)
In this paper, we introduce a fast implementation of the CT EXT algorithm for testor property identification, that is based on an accumulative binary tuple. The fast implementation of the CT EXT algorithm (one of the fastest algorithms reported), is designed to generate all the typical testors from a training matrix, requiring a reduced number of(More)
This article presents an innovative technique for solving the problem of finding the core within a fingerprint. The Radon transform and a tree clustering algorithm were key to locating the coordinates of the core. Binarization and high-pass filtering processes to improve the contrast in fingerprints are proposed. The core of a fingerprint is located in the(More)