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Conference Paper: An empirical study on the visual cluster validation method with Fastmap

TitleAn empirical study on the visual cluster validation method with Fastmap
Authors
KeywordsComputers
Computer systems and computers
Information science and informaiton theory
Issue Date2001
PublisherIEEE.
Citation
The 7th International Conference on Database Systems for Advanced Applications, Hong Kong, China, 18-21 April 2001. In Conference Proceedings, 2001, p. 84-91 How to Cite?
AbstractThis paper presents an empirical study on the visual method for cluster validation based on the Fastmap projection. The visual cluster validation method attempts to tackle two clustering problems in data mining: to verify partitions of data created by a clustering algorithm; and to identify genuine clusters from data partitions. They are achieved through projecting objects and clusters by Fastmap to the 2D space and visually examining the results by humans. A Monte Carlo evaluation of the visual method was conducted. The validation results of the visual method were compared with the results of two internal statistical cluster validation indices, which shows that the visual method is in consistence with the statistical validation methods. This indicates that the visual cluster validation method is indeed effective and applicable to data mining applications.
Persistent Identifierhttp://hdl.handle.net/10722/46603

 

DC FieldValueLanguage
dc.contributor.authorHuang, Zen_HK
dc.contributor.authorCheung, DWLen_HK
dc.contributor.authorNg, MKen_HK
dc.date.accessioned2007-10-30T06:53:59Z-
dc.date.available2007-10-30T06:53:59Z-
dc.date.issued2001en_HK
dc.identifier.citationThe 7th International Conference on Database Systems for Advanced Applications, Hong Kong, China, 18-21 April 2001. In Conference Proceedings, 2001, p. 84-91en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46603-
dc.description.abstractThis paper presents an empirical study on the visual method for cluster validation based on the Fastmap projection. The visual cluster validation method attempts to tackle two clustering problems in data mining: to verify partitions of data created by a clustering algorithm; and to identify genuine clusters from data partitions. They are achieved through projecting objects and clusters by Fastmap to the 2D space and visually examining the results by humans. A Monte Carlo evaluation of the visual method was conducted. The validation results of the visual method were compared with the results of two internal statistical cluster validation indices, which shows that the visual method is in consistence with the statistical validation methods. This indicates that the visual cluster validation method is indeed effective and applicable to data mining applications.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofInternational Conference on Database Systems for Advanced Applications-
dc.rights©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputersen_HK
dc.subjectComputer systems and computersen_HK
dc.subjectInformation science and informaiton theoryen_HK
dc.titleAn empirical study on the visual cluster validation method with Fastmapen_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/DASFAA.2001.916368en_HK
dc.identifier.hkuros58038-

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