Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6412
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kılıç, Kemal | - |
dc.contributor.author | Uncu, Öze | - |
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.date.accessioned | 2021-09-11T15:36:20Z | - |
dc.date.available | 2021-09-11T15:36:20Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://doi.org/10.1016/j.ins.2007.06.030 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6412 | - |
dc.description.abstract | Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and offer a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classification problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches. (C) 2007 Elsevier Inc. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science Inc | en_US |
dc.relation.ispartof | Information Sciences | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | fuzzy system modelling | en_US |
dc.subject | medicine | en_US |
dc.subject | knowledge acquisition | en_US |
dc.subject | data mining | en_US |
dc.subject | structure identification | en_US |
dc.title | Comparison of Different Strategies of Utilizing Fuzzy Clustering in Structure Identification | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Industrial Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 177 | en_US |
dc.identifier.issue | 23 | en_US |
dc.identifier.startpage | 5153 | en_US |
dc.identifier.endpage | 5162 | en_US |
dc.authorid | 0000-0003-4506-9434 | - |
dc.identifier.wos | WOS:000250285400003 | en_US |
dc.identifier.scopus | 2-s2.0-34548604303 | en_US |
dc.institutionauthor | Türkşen, İsmail Burhan | - |
dc.identifier.doi | 10.1016/j.ins.2007.06.030 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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