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dc.contributor.authorSchmidberger, Gabi
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Porto, Portugalen_NZ
dc.date.accessioned2008-11-21T02:24:39Z
dc.date.available2008-11-21T02:24:39Z
dc.date.issued2005
dc.identifier.citationSchmidberger, G. & Frank, E. (2005). Unsupervised discretization using tree-based density estimation. In A. Jorge et al. (Eds), Proceedings of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. (pp. 240-251). Berlin: Springer.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1444
dc.description.abstractThis paper presents an unsupervised discretization method that performs density estimation for univariate data. The subintervals that the discretization produces can be used as the bins of a histogram. Histograms are a very simple and broadly understood means for displaying data, and our method automatically adapts bin widths to the data. It uses the log-likelihood as the scoring function to select cut points and the cross-validated log-likelihood to select the number of intervals. We compare this method with equal-width discretization where we also select the number of bins using the cross-validated log-likelihood and with equal-frequency discretization.en_US
dc.language.isoen
dc.publisherSpringer, Berlinen_US
dc.relation.urihttp://www.springerlink.com/content/y57346t740438t7x/en_US
dc.sourcePKDD 2005en_NZ
dc.subjectcomputer scienceen_US
dc.subjectunsupervised discretizationen_US
dc.titleUnsupervised discretization using tree-based density estimationen_US
dc.typeConference Contributionen_US
dc.identifier.doi10.1007/11564126_26en_US
dc.relation.isPartOfProc 9th European Conference on Principles and Practice of Knowledge Discovery in Databasesen_NZ
pubs.begin-page240en_NZ
pubs.elements-id15872
pubs.end-page251en_NZ
pubs.finish-date2005-10-07en_NZ
pubs.place-of-publicationGermanyen_NZ
pubs.start-date2005-10-03en_NZ
pubs.volumeLNCS 3721en_NZ


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