dc.rights.license | Kūrybinių bendrijų licencija / Creative Commons licence | en_US |
dc.contributor.author | Aghdaie, Mohammad Hasan | |
dc.contributor.author | Hashemkhani Zolfani, Sarfaraz | |
dc.contributor.author | Zavadskas, Edmundas Kazimieras | |
dc.date.accessioned | 2024-10-17T07:29:06Z | |
dc.date.available | 2024-10-17T07:29:06Z | |
dc.date.issued | 2014 | |
dc.identifier.issn | 1877-0428 | en_US |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/155276 | |
dc.description.abstract | Data 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.extent | 10 p. | en_US |
dc.format.medium | Tekstas / Text | en_US |
dc.language.iso | en | en_US |
dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/155081 | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source.uri | https://www.sciencedirect.com/science/article/pii/S1877042813055614 | en_US |
dc.subject | data mining | en_US |
dc.subject | Multiple Attribute Decision Making (MADM) | en_US |
dc.subject | Clustering | en_US |
dc.subject | SWARA | en_US |
dc.subject | VIKOR | en_US |
dc.subject | Supplier clustering and ranking | en_US |
dc.title | Synergies of data mining and multiple attribute decision making | en_US |
dc.type | Konferencijos publikacija / Conference paper | en_US |
dcterms.accessRights | Laisvai prieinamas / Openly available | en_US |
dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
dcterms.issued | 2014-01-24 | |
dcterms.license | CC BY NC ND | en_US |
dcterms.references | 22 | en_US |
dc.description.version | Taip / Yes | en_US |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | en_US |
dc.contributor.institution | Vilnius Gediminas Technical University | en_US |
dc.contributor.institution | Shomal University | en_US |
dc.contributor.institution | Amirkabir University of Technology | en_US |
dc.contributor.faculty | Internetinių ir intelektualiųjų technologijų institutas / Institute of Internet and Intelligent Technologies | |
dcterms.sourcetitle | Procedia - Social and Behavioral Sciences | en_US |
dc.description.volume | vol. 110 | en_US |
dc.publisher.name | Elsevier | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.sbspro.2013.12.921 | en_US |