DC Field | Value | Language |
---|---|---|
dc.contributor.author | Spyridakos, Athanasios | - |
dc.date.accessioned | 2024-04-02T09:38:34Z | - |
dc.date.available | 2024-04-02T09:38:34Z | - |
dc.date.issued | 2010-01-01 | - |
dc.identifier | scopus-77956030580 | - |
dc.identifier.isbn | 978-1-60750-577-8 | - |
dc.identifier.other | 77956030580 | - |
dc.identifier.uri | https://uniwacris.uniwa.gr/handle/3000/1719 | - |
dc.description.abstract | The selection of the reference set in the frame of Disaggregation - Aggregation (D-A) methods (UTA*, UTA II, UTADIS), constitutes one of the most important steps of the process, while it influences the accuracy and reliability of the assessed preference model. The reference set ought to satisfy two conditions: a) the alternatives of the reference set should be familiar to the Decision Maker (DM) so as to express his/her preferences from a known situation; and b) the selected alternatives have to be representative of the total set, so that all the different points of view of the decision space to be taken into consideration. This paper presents a clustering technique that is embedded in the Multicriteria Decision Aid Systems MINORA and MIIDAS, which incorporates threshold of dissimilarity in order to support DMs or Decision Analysts (DAs) to select a representative reference set. This technique is compared with the most familiar multivariate data analysis clustering methods, such as k-means and hierarchical. Also, the technique is illustrated through a real world case study. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Bridging the Socio-technical Gap in Decision Support Systems | en_US |
dc.relation.ispartofseries | Frontiers in Artificial Intelligence and Applications | en_US |
dc.subject | Decision support systems | en_US |
dc.subject | Disaggregation - aggregation multicriteria Approach | en_US |
dc.subject | Multivariate data analysis | en_US |
dc.title | Selecting reference set in Disaggregation Aggregation Multicriteria Decision Aid approach utilizing clustering techniques with dissimilarity thresholds | en_US |
dc.type | Book Chapter | en_US |
dc.identifier.doi | 10.3233/978-1-60750-577-8-237 | en_US |
dc.identifier.scopus | 2-s2.0-77956030580 | - |
dcterms.accessRights | 0 | en_US |
dc.relation.dept | Department of Business Administration | en_US |
dc.relation.faculty | School of Administrative, Economics and Social Sciences | en_US |
dc.identifier.spage | 237 | en_US |
dc.identifier.epage | 248 | en_US |
dc.collaboration | University of West Attica (UNIWA) | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.journals | Subscription | en_US |
dc.publication | Peer Reviewed | en_US |
dc.country | Greece | en_US |
local.metadatastatus | verified | en_US |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | Book Chapter | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
crisitem.author.dept | Department of Business Administration | - |
crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
Appears in Collections: | Book Chapter / Κεφάλαιο Βιβλίου |
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