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dc.contributor.authorEstivill-Castro, V
dc.contributor.authorYang, J
dc.contributor.editorRiichiro Mizoguchi, John K. Slaney
dc.date.accessioned2018-03-22T05:43:34Z
dc.date.available2018-03-22T05:43:34Z
dc.date.issued2000
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/3-540-44533-1_24
dc.identifier.urihttp://hdl.handle.net/10072/131887
dc.description.abstractGeneral purpose and highly applicable clustering methods are required for knowledge discovery. k-Means has been adopted as the prototype of iterative model-based clustering because of its speed, simplicity and capability to work within the format of very large databases. However, k-MEANS has several disadvantages derived from its statistical simplicity. We propose algorithms that remain very efficient, generally applicable, multidimensional but are more robust to noise and outliers. We achieve this by using medians rather than means as estimators of centers of clusters. Comparison with k-Means, EM and Gibbs sampling demonstrates the advantages of our algorithms.
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeBerlin
dc.relation.ispartofconferencename6th Pacific Rim International Conference on Artificial Intelligence
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2000-08-28
dc.relation.ispartofdateto2000-09-01
dc.relation.ispartoflocationMelbourne Australia
dc.relation.ispartofpagefrom208
dc.relation.ispartofpageto218
dc.relation.ispartofvolume1886
dc.subject.fieldofresearchcode280207
dc.titleFast and Robust General Purpose Clustering Algorithms
dc.typeConference output
dc.type.descriptionE2 - Conferences (Non Refereed)
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorEstivill-Castro, Vladimir


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    Contains papers delivered by Griffith authors at national and international conferences.

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