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dc.contributor.authorYuliani Setia Dewi
dc.date.accessioned2014-04-01T23:48:45Z
dc.date.available2014-04-01T23:48:45Z
dc.date.issued2014-04-01
dc.identifier.issn1411-6669
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/56609
dc.description.abstractVarious clustering algorithms have been developed to group data into clusters. This paper describes clustering objects with mixed categorical and numeric data types. The methods used are two step clustering and transforms mixed data using nonlinear principal component analysis then groups the output resulted using hierarchical aglomerative clustering. The results show that the number of optimal cluster using both methods have the same optimal number of cluster but the rank of ratios of distance measure and distribution of cluster membership are different.en_US
dc.language.isootheren_US
dc.relation.ispartofseriesMajalah Ilmiah Matematika dan Statistika;Volume 13, Juni 2013
dc.subjectTwo step clustering, nonlinear principal component analysis, mixed data, transformen_US
dc.titleANALISIS CLUSTER UNTUK DATA CAMPURAN KATEGORIK DAN NUMERIK 80 (Cluster Analysis for Mixed Cagtegorical and Numeric Data Types)en_US
dc.typeArticleen_US


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