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dc.contributor.authorWang, L
dc.contributor.authorLeckie, C
dc.contributor.authorRamamohanarao, K
dc.contributor.authorBezdek, J
dc.date.available2014-05-21T22:49:12Z
dc.date.issued2009-03-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000262560100003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationWang, L., Leckie, C., Ramamohanarao, K. & Bezdek, J. (2009). Automatically Determining the Number of Clusters in Unlabeled Data Sets. IEEE Transactions on Knowledge and Data Engineering, 21 (3), pp.335-350. https://doi.org/10.1109/TKDE.2008.158.
dc.identifier.issn1041-4347
dc.identifier.urihttp://hdl.handle.net/11343/29275
dc.description.abstractOne of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper, we investigate a new method called Dark Block Extraction (DBE) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for Visual Assessment of Cluster Tendency (VAT) of a data set, using several common image and signal processing techniques. Its basic steps include 1) generating a VAT image of an input dissimilarity matrix, 2) performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering, 3) applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal, and 4) smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our DBE method is nearly “automatic,” depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subjectArtificial Intelligence and Image Processing
dc.titleAutomatically Determining the Number of Clusters in Unlabeled Data Sets
dc.typeJournal Article
dc.identifier.doi10.1109/TKDE.2008.158
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleIEEE Transactions on Knowledge and Data Engineering
melbourne.source.volume21
melbourne.source.issue3
melbourne.source.pages335-350
dc.description.pagestart335
melbourne.publicationid131745
melbourne.elementsid314211
melbourne.contributor.authorWANG, LIANG
melbourne.contributor.authorLeckie, Christopher
melbourne.contributor.authorKotagiri, Ramamohanarao
melbourne.contributor.authorBezdek, James
melbourne.internal.ingestnoteAbstract bulk upload (2017-07-20)
dc.identifier.eissn1558-2191
melbourne.accessrightsThis item is currently not available from this repository


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