A clustering framework for unbalanced partitioning and outlier filtering on high dimensional datasets

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dc.authorid0000-0002-9245-5728en_US
dc.contributor.authorBilgin, Turgay Tugay
dc.contributor.authorÇamurcu, A. Yılmaz
dc.date.accessioned2024-07-12T20:57:58Z
dc.date.available2024-07-12T20:57:58Z
dc.date.issued2007en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description11th East European Conference on Advances in Databases and Information Systems, ADBIS 2007 -- 29 September 2007 through 3 October 2007 -- Varna -- 71032en_US
dc.description.abstractIn this study, we propose a better relationship based clustering framework for dealing with unbalanced clustering and outlier filtering on high dimensional datasets. Original relationship based clustering framework is based on a weighted graph partitioning system named METIS. However, it has two major drawbacks: no outlier filtering and forcing clusters to be balanced. Our proposed framework uses Graclus, an unbalanced kernel k-means based partitioning system. We have two major improvements over the original framework: First, we introduce a new space. It consists of tiny unbalanced partitions created using Graclus, hence we call it micro-partition space. We use a filtering approach to drop out singletons or micro-partitions that have fewer members than a threshold value. Second, we agglomerate the filtered micro-partition space and apply Graclus again for clustering. The visualization of the results has been carried out by CLUSION. Our experiments have shown that our proposed framework produces promising results on high dimensional datasets. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.identifier.citationBilgin, T. T. ve Çamurcu, A. Y. (2007). A clustering framework for unbalanced partitioning and outlier filtering on high dimensional datasets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). s. 205-216.en_US
dc.identifier.endpage216en_US
dc.identifier.isbn9783540751847
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-38049000466en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage205en_US
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-540-75185-4_16
dc.identifier.urihttps://hdl.handle.net/20.500.12415/3111
dc.identifier.volume4690 LNCSen_US
dc.identifier.wosWOS:000250283400016en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08239
dc.subjectClusteringen_US
dc.subjectData miningen_US
dc.subjectDimensionalityen_US
dc.subjectOutlier filteringen_US
dc.titleA clustering framework for unbalanced partitioning and outlier filtering on high dimensional datasetsen_US
dc.typeConference Object
dspace.entity.typePublication

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