Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/27005
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKayalıgil, Sinan-
dc.contributor.authorÖzdemirel, Nur Evin-
dc.date.accessioned2022-06-09T12:45:01Z-
dc.date.available2022-06-09T12:45:01Z-
dc.date.issued2015-03-
dc.identifier.citationİnkaya, T. vd. (2015). "Ant Colony Optimization based clustering methodology". Applied Soft Computing, 28, 301-311.en_US
dc.identifier.issn1568-4946-
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2014.11.060-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1568494614006334-
dc.identifier.urihttp://hdl.handle.net/11452/27005-
dc.description.abstractIn this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two objective functions, namely adjusted compactness and relative separation. Each objective function evaluates the clustering solution with respect to the local characteristics of the neighborhoods. This allows us to measure the quality of a wide range of clustering solutions without a priori information. Next, using the two objective functions we present a novel clustering methodology based on Ant Colony Optimization (ACO-C). ACO-C works in a multi-objective setting and yields a set of non-dominated solutions. ACO-C has two pre-processing steps: neighborhood construction and data set reduction. The former extracts the local characteristics of data points, whereas the latter is used for scalability. We compare the proposed methodology with other clustering approaches. The experimental results indicate that ACO-C outperforms the competing approaches. The multi-objective evaluation mechanism relative to the neighborhoods enhances the extraction of the arbitrary-shaped clusters having density variations.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAnt Colony Optimizationen_US
dc.subjectClusteringen_US
dc.subjectData set reductionen_US
dc.subjectMultiple objectivesen_US
dc.subjectAutomatic evolutionen_US
dc.subjectK-meansen_US
dc.subjectAlgorithmen_US
dc.subjectHybridizationen_US
dc.subjectDensityen_US
dc.subjectComputer scienceen_US
dc.subjectCluster analysisen_US
dc.subjectFunction evaluationen_US
dc.subjectReductionen_US
dc.subjectClusteringen_US
dc.subjectClustering solutionsen_US
dc.subjectData seten_US
dc.subjectLocal characteristicsen_US
dc.subjectMulti-objective evaluationsen_US
dc.subjectMultiple-objectivesen_US
dc.subjectNeighborhood constructionen_US
dc.subjectNondominated solutionsen_US
dc.subjectAnt colony optimizationen_US
dc.titleAnt Colony Optimization based clustering methodologyen_US
dc.typeArticleen_US
dc.identifier.wos000348452500030tr_TR
dc.identifier.scopus2-s2.0-84919930171tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.tr_TR
dc.contributor.orcid0000-0002-6260-0162tr_TR
dc.identifier.startpage301tr_TR
dc.identifier.endpage311tr_TR
dc.identifier.volume28tr_TR
dc.relation.journalApplied Soft Computingen_US
dc.contributor.buuauthorİnkaya, Tülin-
dc.contributor.researcheridAAH-2155-2021tr_TR
dc.relation.collaborationYurt içitr_TR
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosComputer science, interdisciplinary applicationsen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ1en_US
dc.contributor.scopusid24490728300tr_TR
dc.subject.scopusData Clustering; K-Mean Algorithm; Cluster Analysisen_US
Appears in Collections:Scopus
Web of Science

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.