Attribute Value Weighting in K-Modes Clustering

Simple item page
dc.creatorHe, Zengyou
dc.creatorXu, Xaiofei
dc.creatorDeng, Shengchun
dc.date2007-01-03
dc.date.accessioned2026-07-07T07:38:11Z
dc.date.available2026-07-07T07:38:11Z
dc.descriptionIn this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.
dc.description15 pages
dc.identifierhttps://arxiv.org/abs/cs/0701013
dc.identifierhttp://arxiv.org/abs/cs/0701013
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/121029
dc.subjectArtificial Intelligence
dc.titleAttribute Value Weighting in K-Modes Clustering
dc.typetext

Files

Collections

arXiv