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dc.contributor.authorMoggridge, Paul
dc.contributor.authorHelian, Na
dc.contributor.authorSun, Yi
dc.contributor.authorLilley, Mariana
dc.contributor.authorVeneziano, Vito
dc.contributor.editorIliadis, Lazaros
dc.contributor.editorAngelov, Plamen Parvanov
dc.contributor.editorJayne, Chrisina
dc.contributor.editorPimenidis, Elias
dc.date.accessioned2020-10-13T00:04:28Z
dc.date.available2020-10-13T00:04:28Z
dc.date.issued2020-05-28
dc.identifier.citationMoggridge , P , Helian , N , Sun , Y , Lilley , M & Veneziano , V 2020 , Instance Weighted Clustering: Local Outlier Factor and K-Means . in L Iliadis , P P Angelov , C Jayne & E Pimenidis (eds) , Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference : Proceedings of the EANN 2020 . Proceedings of the International Neural Networks Society , Springer , pp. 435-446 . https://doi.org/10.1007/978-3-030-48791-1_34
dc.identifier.isbn9783030487904
dc.identifier.isbn9783030487911
dc.identifier.issn2661-8141
dc.identifier.otherPURE: 22760161
dc.identifier.otherPURE UUID: fd1a468b-0a4f-4c76-96f9-562fa5d6d601
dc.identifier.otherORCID: /0000-0001-6687-0306/work/82133004
dc.identifier.urihttp://hdl.handle.net/2299/23248
dc.description© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-48791-1_34.
dc.description.abstractClustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.en
dc.format.extent12
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofProceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
dc.relation.ispartofseriesProceedings of the International Neural Networks Society
dc.titleInstance Weighted Clustering: Local Outlier Factor and K-Meansen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSPECS Deans Group
dc.date.embargoedUntil2022-05-28
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-030-48791-1_34
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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