Weighted adjacent matrix for K-means clustering

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dc.citation.issue23
dc.citation.volume78
dc.contributor.authorZhou J
dc.contributor.authorLiu T
dc.contributor.authorZhu J
dc.date.available2019-12
dc.date.issued2019-12
dc.descriptionCAUL read and publish agreement 2022
dc.description.abstractK-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.
dc.description.publication-statusPublished
dc.format.extent33415 - 33434
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000500000600039&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationMULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (23), pp. 33415 - 33434
dc.identifier.doi10.1007/s11042-019-08009-x
dc.identifier.eissn1573-7721
dc.identifier.elements-id425429
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1380-7501
dc.identifier.urihttps://hdl.handle.net/10179/17429
dc.publisherSpringer Science+Business Media, LLC
dc.relation.isPartOfMULTIMEDIA TOOLS AND APPLICATIONS
dc.subjectk-means clustering
dc.subjectSimilarity measurement
dc.subjectAdjacent matrix
dc.subjectUnsupervised learning
dc.subject.anzsrc0803 Computer Software
dc.subject.anzsrc0805 Distributed Computing
dc.subject.anzsrc0806 Information Systems
dc.subject.anzsrc0801 Artificial Intelligence and Image Processing
dc.titleWeighted adjacent matrix for K-means clustering
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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