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Conference Paper: M-FastMap: A modified fastmap algorithm for visual cluster validation in data mining

TitleM-FastMap: A modified fastmap algorithm for visual cluster validation in data mining
Authors
Issue Date2002
PublisherSpringer.
Citation
6th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002), Taipei, Taiwan, 6-8 May 2002. In Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002: Proceedings, 2002 , p. 224-236 How to Cite?
Abstract© Springer-Verlag Berlin Heidelberg 2002. This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/276702
ISBN
ISSN
2005 Impact Factor: 0.302
2020 SCImago Journal Rankings: 0.249
Series/Report no.Lecture Notes in Computer Science ; 2336

 

DC FieldValueLanguage
dc.contributor.authorNg, Michael-
dc.contributor.authorHuang, Joshua-
dc.date.accessioned2019-09-18T08:34:24Z-
dc.date.available2019-09-18T08:34:24Z-
dc.date.issued2002-
dc.identifier.citation6th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002), Taipei, Taiwan, 6-8 May 2002. In Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002: Proceedings, 2002 , p. 224-236-
dc.identifier.isbn9783540437048-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276702-
dc.description.abstract© Springer-Verlag Berlin Heidelberg 2002. This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 2336-
dc.titleM-FastMap: A modified fastmap algorithm for visual cluster validation in data mining-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-47887-6_22-
dc.identifier.scopuseid_2-s2.0-84945289693-
dc.identifier.spage224-
dc.identifier.epage236-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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