Clustering large datasets using cobweb and K-means in tandem

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dc.contributor.authorLi, Mi
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Cairns, Australiaen_NZ
dc.date.accessioned2008-11-25T03:24:07Z
dc.date.available2008-11-25T03:24:07Z
dc.date.issued2005
dc.description.abstractThis paper presents a single scan algorithm for clustering large datasets based on a two phase process which combines two well known clustering methods. The Cobweb algorithm is modified to produce a balanced tree with subclusters at the leaves, and then K-means is applied to the resulting subclusters. The resulting method, Scalable Cobweb, is then compared to a single pass K-means algorithm and standard K-means. The evaluation looks at error as measured by the sum of squared error and vulnerability to the order in which data points are processed.en_US
dc.identifier.citationLi, M., Holmes, G. & Pfahringer, B. (2005). Clustering large datasets using cobweb and K-means in tandem. In G.I. Webb & Xinghuo Yu(Eds.), Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. (pp. 368-379). Berlin: Springer.en_US
dc.identifier.doi10.1007/978-3-540-30549-1_33en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/1461
dc.language.isoen
dc.publisherSpringer, Berlinen_US
dc.relation.isPartOfAdvances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligenceen_NZ
dc.relation.urihttp://www.springerlink.com/content/w4rtl83lxfx874l7/en_US
dc.sourceAI 2004en_NZ
dc.subjectcomputer scienceen_US
dc.subjectK-meansen_US
dc.subjectMachine learning
dc.titleClustering large datasets using cobweb and K-means in tandemen_US
dc.typeConference Contributionen_US
pubs.begin-page368en_NZ
pubs.elements-id15226
pubs.end-page379en_NZ
pubs.finish-date2004-12-06en_NZ
pubs.place-of-publicationHeidelbergen_NZ
pubs.start-date2004-12-04en_NZ
pubs.volumeLNAI 3339en_NZ
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