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dc.contributor.authorMuhammad Fuad, Muhammad Marwan
dc.date.accessioned2016-03-16T09:39:39Z
dc.date.available2016-03-16T09:39:39Z
dc.date.issued2015
dc.description.abstractFuzzy c-means clustering (FCM) is a clustering method which is based on the partial membership concept. As with the other clustering methods, FCM applies a distance to cluster the data. While the Euclidean distance is widely-used to perform the clustering task, other distances have been suggested in the literature. In this paper we study the use of a weighted combination of metrics in FCM clustering of time series where the weights in the combination are the outcome of an optimization process using differential evolution, genetic algorithms, and particle swarm optimization as optimizers. We show how the overfitting phenomenon interferes in the optimization process that the optimal results obtained during the training stage degrade during the testing stage as a result of overfitting.en_US
dc.descriptionPublished version. Source at <a href=http://doi.org/10.5220/0005276203480353>http://doi.org/10.5220/0005276203480353</a>.en_US
dc.identifier.citationDe Marsico, Maria; Figueiredo, Mario; Fred, Ana [Eds.] ICPRAM 2015 Proceedings of the International Conference on Pattern Recognition Applications and Methods Volum 1 p. 348-353, SciTePress, 2015en_US
dc.identifier.cristinIDFRIDAID 1299697
dc.identifier.doi10.5220/0005276203480353
dc.identifier.isbn978-989-758-076-5
dc.identifier.urihttps://hdl.handle.net/10037/8980
dc.identifier.urnURN:NBN:no-uit_munin_8541
dc.language.isoengen_US
dc.publisherINSTICCen_US
dc.rights.accessRightsopenAccess
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.subjectData Miningen_US
dc.subjectDifferential Evolutionen_US
dc.subjectDistance Metricsen_US
dc.subjectFuzzy C-Means Clusteringen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectOverfittingen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectTime Seriesen_US
dc.titleOn the Application of Bio-Inspired Optimization Algorithms to Fuzzy C-Means Clustering of Time Seriesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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