A modified relationship based clustering framework for density based clustering and outlier filtering on high dimensional datasets
dc.contributor.author | Bilgin, Turgay Tugay | |
dc.contributor.author | Camurcu, A. Yilmaz | |
dc.contributor.editor | Zhou, ZH; Li, H; Yang, Q | |
dc.date.accessioned | 2024-07-12T21:59:44Z | |
dc.date.available | 2024-07-12T21:59:44Z | |
dc.date.issued | 2007 | en_US |
dc.department | Maltepe Üniversitesi, Rektörlük | en_US |
dc.description | 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining -- MAY 22-25, 2007-5007 -- Nanjing, PEOPLES R CHINA | en_US |
dc.description.abstract | In this study, we propose a modified version of relationship based clustering framework dealing with density based clustering and outlier detection in high dimensional datasets. Originally, relationship based clustering framework is based on METIS. Therefore, it has some drawbacks such as no outlier detection and difficulty of determining the number of clusters. We propose two improvements over the framework. First, we introduce a new space which consists of tiny partitions created by METIS, hence we call it micro-partition space. Second, we used DBSCAN for clustering micro-partition space. The visualization of the results are carried out by CLUSION. Our experiments have shown that, our proposed framework produces promising results on high dimensional datasets. | en_US |
dc.description.sponsorship | Nanjing Univ, LAMDA Grp, Nanjing Univ Aeronaut & Astronaut, Japanese Soc Artificial Intelligence, Singapore Inst Stat, Natl Nat Sci Fdn China, Microsoft adCenter Labs, NEC Labs China, Microsoft Res Asia, Salford Syst, KC Wong Educ Fdn | en_US |
dc.description.sponsorship | Marmara University Scientific Research Projects Committee (BAPKO) [FEN DKR-270306-0055] | en_US |
dc.description.sponsorship | The research for this article was supported by grant number FEN DKR-270306-0055 from Marmara University Scientific Research Projects Committee (BAPKO). | en_US |
dc.identifier.endpage | + | en_US |
dc.identifier.isbn | 978-3-540-71700-3 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.scopus | 2-s2.0-38049136657 | en_US |
dc.identifier.scopusquality | Q3 | en_US |
dc.identifier.startpage | 409 | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12415/8971 | |
dc.identifier.volume | 4426 | en_US |
dc.identifier.wos | WOS:000246475100040 | en_US |
dc.identifier.wosquality | N/A | en_US |
dc.indekslendigikaynak | Web of Science | |
dc.indekslendigikaynak | Scopus | |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER-VERLAG BERLIN | en_US |
dc.relation.ispartof | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | en_US |
dc.relation.isversionof | Lecture Notes in Computer Science | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.snmz | KY06017 | |
dc.title | A modified relationship based clustering framework for density based clustering and outlier filtering on high dimensional datasets | en_US |
dc.type | Conference Object | |
dspace.entity.type | Publication |