Clustering mixed data

Lynette Hunt; Murray Jorgensen

doi:10.1002/widm.33

journal article May 20, 2011

Clustering mixed data

Lynette Hunt Murray Jorgensen

WIREs Data Mining and Knowledge Discovery Vol. 1 No. 4 pp. 352-361 · Wiley

View at Publisher Save 10.1002/widm.33

Abstract

AbstractMixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case ofmixed data: multivariate data sets that contain both continuous and categorical attributes. © 2011 John Wiley & Sons, Inc.WIREs Data Mining Knowl Discov2011 1 352–361 DOI: 10.1002/widm.33This article is categorized under:Algorithmic Development > Structure DiscoveryTechnologies > Structure Discovery and Clustering

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References

Details

Published: May 20, 2011
Vol/Issue: 1(4)
Pages: 352-361
License: View

Authors

Cite This Article

Lynette Hunt, Murray Jorgensen (2011). Clustering mixed data. WIREs Data Mining and Knowledge Discovery, 1(4), 352-361. https://doi.org/10.1002/widm.33

Clustering mixed data

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