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dc.rights.licenseKūrybinių bendrijų licencija / Creative Commons licenceen_US
dc.contributor.authorAghdaie, Mohammad Hasan
dc.contributor.authorHashemkhani Zolfani, Sarfaraz
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.date.accessioned2024-10-17T07:29:06Z
dc.date.available2024-10-17T07:29:06Z
dc.date.issued2014
dc.identifier.issn1877-0428en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/155276
dc.description.abstractData Mining (DM) and Multiple Attribute Decision Making (MADM) are two fast growing trends in Operations Research (OR) /Management Science (MS). In this article, we identify the synergies of data mining and MADM. Synergies can be attained by integration of MADM techniques into data mining and vice versa. The primary goal of the paper is to show a wide range of interactions between these two fields from a new perspective with an example of the integrated approach in supplier clustering and ranking. The integrated approach includes cluster analysis as a data mining tool and Step-wise Weight Assessment Ratio Analysis (SWARA) and VIseKriterijumskao ptimizacija i KOmpromisno Resenje (VIKOR) as the two MADM tools. More precisely, the features for clustering were selected and weighted by SWARA method and suppliers are clustered using two-stage cluster analysis. In addition, VIKOR technique is used to rank the clusters from the best to the worst one. The proposed integrated approach is presented to demonstrate the applicability of the proposed methodology.en_US
dc.format.extent10 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/155081en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.source.urihttps://www.sciencedirect.com/science/article/pii/S1877042813055614en_US
dc.subjectdata miningen_US
dc.subjectMultiple Attribute Decision Making (MADM)en_US
dc.subjectClusteringen_US
dc.subjectSWARAen_US
dc.subjectVIKORen_US
dc.subjectSupplier clustering and rankingen_US
dc.titleSynergies of data mining and multiple attribute decision makingen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accessRightsLaisvai prieinamas / Openly availableen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2014-01-24
dcterms.licenseCC BY NC NDen_US
dcterms.references22en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dc.contributor.institutionShomal Universityen_US
dc.contributor.institutionAmirkabir University of Technologyen_US
dc.contributor.facultyInternetinių ir intelektualiųjų technologijų institutas / Institute of Internet and Intelligent Technologies
dcterms.sourcetitleProcedia - Social and Behavioral Sciencesen_US
dc.description.volumevol. 110en_US
dc.publisher.nameElsevieren_US
dc.identifier.doihttps://doi.org/10.1016/j.sbspro.2013.12.921en_US


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Kūrybinių bendrijų licencija / Creative Commons licence
Except where otherwise noted, this item's license is described as Kūrybinių bendrijų licencija / Creative Commons licence