Gerasimos Antzoulatos

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A novel methodology for unsupervised data clustering based on Evolutionary Computation, named “Intelligent Unsupervised Clustering” (IUC) is introduced. IUC searches for the “optimal clusters’ representatives” using Evolutionary Algorithms (EAs) and utilising a Window Density Function (WDF) as an objective function. EAs ensure that the representative is(More)
OBJECTIVE To report for the first time a case series of vitreoretinal pathologic conditions after laser in situ keratomileusis (LASIK) and to determine its incidence. DESIGN Case series. PARTICIPANTS Five refractive surgeons and 29,916 eyes that underwent surgical correction of ametropia (83.2% were myopic) ranging from -0.75 to -29.00 diopters (D;(More)
PURPOSE To report a case series of rhegmatogenous retinal detachment (RRD) after laser-assisted in situ keratomileusis (LASIK) and its incidence at a mean of 24 months. METHODS The clinical charts of patients who experienced RRD after LASIK were reviewed. Five refractive surgeons and 24,890 myopic eyes that underwent surgical correction of myopia ranging(More)
Contrary to much of the research in machine learning where there is a concentration on problems with relatively small volume of data, one of the main challenges of the today's data mining systems is their ability to handle data that is substantially larger than available main memory on a single processor. In this paper, we present a distributed technique(More)
PURPOSE To report the characteristics and frequency of rhegmatogenous retinal detachment (RRD) after laser in situ keratomileusis (LASIK) for the correction of myopia in a large case series. SETTING Private practices, Caracas, Venezuela. METHODS Five refractive surgeons and 31 739 myopic eyes that had surgical correction of a mean myopia of -6.01(More)
Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard(More)
In spite of the increasing interest into clustering research within the last decades, a unified clustering theory that is independent of a particular algorithm, or underlying the data structure and even the objective function has not be formulated so far. In the paper at hand, we take the first steps towards a theoretical foundation of clustering, by(More)
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