A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets

dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.date.accessioned2019-05-21T08:06:38Z
dc.date.available2019-05-21T08:06:38Z
dc.date.issued2017-07-30
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.en_US
dc.identifier.citationErtuğrul, Ö. F. (2017). A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets. Turkish Journal of Electrical Engineering & Computer Sciences, 25 (4), pp. 2614 – 2634. https://doi.org/10.3906/elk-1602-341en_US
dc.identifier.endpage2634en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2614en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2018
dc.identifier.volume25en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTÜBİTAKen_US
dc.relation.isversionof10.3906/elk-1602-341en_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectData Condensingen_US
dc.subjectPrototype Extractingen_US
dc.subjectClusteringen_US
dc.subjectMassive Datasetsen_US
dc.subjectTime-Ordered Datasetsen_US
dc.titleA novel approach for extracting ideal exemplars by clustering for massive time-ordered datasetsen_US
dc.typeArticleen_US

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