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dc.contributor.authorEstivill-Castro, V
dc.contributor.authorLee, I
dc.contributor.editorPaul A. Longley
dc.date.accessioned2017-05-03T14:15:48Z
dc.date.available2017-05-03T14:15:48Z
dc.date.issued2002
dc.date.modified2009-04-02T07:14:56Z
dc.identifier.issn0198-9715
dc.identifier.doi10.1016/S0198-9715(01)00044-8
dc.identifier.urihttp://hdl.handle.net/10072/22041
dc.description.abstractMinimizing the need for user-specified arguments results in less costly Geographical Data Mining. For massive data sets, the need to find best-fit arguments in semi-automatic clustering is not the only concern, the manipulation of data to find arguments opposes the philosophy of ''let the data speak for themselves'' that underpins exploratory data analysis. Our new approach consists of effective and efficient methods for discovering cluster boundaries in point-data sets. Parameters are not specified by users. Rather, values for parameters are revealed from the proximity structures of Voronoi modeling, and thus, an algorithm, AUTOCLUST, calculates them from the Delunay Diagram. We detect clusters of different densities and sparse clusters near to high-density clusters. Multiple bridges linking clusters are identified and removed. All this within O(n log n) time, where n is the number of data points. We contrast AUTOCLUST with algorithms for clustering large georeferenced sets of points. These comparisons confirm the virtues of our approach.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier Science
dc.publisher.placeNetherlands
dc.publisher.urihttp://www.sciencedirect.com/science/journal/01989715
dc.relation.ispartofpagefrom315
dc.relation.ispartofpageto334
dc.relation.ispartofissue4
dc.relation.ispartofjournalComputers, Environment and Urban Systems
dc.relation.ispartofvolume26
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchUrban and regional planning
dc.subject.fieldofresearchcode4013
dc.subject.fieldofresearchcode3304
dc.titleArgument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2002
gro.hasfulltextNo Full Text
gro.griffith.authorEstivill-Castro, Vladimir


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