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dc.contributor.authorBlekas, K.en
dc.contributor.authorLagaris, I. E.en
dc.rightsDefault Licence-
dc.subjectmolecular dynamicsen
dc.subjectglobal optimizationen
dc.subjectorder statisticsen
dc.subjectem algorithmen
dc.titleNewtonian clustering: An approach based on molecular dynamics and global optimizationen
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/j.patcog.2006.07.012-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.abstractGiven a data set, a dynamical procedure is applied to the data points in order to shrink and separate, possibly overlapping clusters. Namely, Newton's equations of motion are employed to concentrate the data points around their cluster centers, using an attractive potential, constructed specially for this purpose. During this process, important information is gathered concerning the spread of each cluster. In succession this information is used to create an objective function that maps each cluster to a local maximum. Global optimization is then used to retrieve the positions of the maxima that correspond to the locations of the cluster centers. Further refinement is achieved by applying the EM-algorithm to a Gaussian mixture model whose construction and initialization is based on the acquired information. To assess the effectiveness of our method, we have conducted experiments on a plethora of benchmark data sets. In addition we have compared its performance against four clustering techniques that are well established in the literature. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.en
heal.journalNamePattern Recognitionen
heal.journalTypepeer reviewed-
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