K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset

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dc.creatorHe, Zengyou
dc.creatorXu, Xiaofei
dc.creatorDeng, Shengchun
dc.creatorDong, Bin
dc.date2005-09-13
dc.date.accessioned2026-07-07T03:23:26Z
dc.date.available2026-07-07T03:23:26Z
dc.descriptionClustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process. Experimental results on real datasets show that k-histogram algorithm can produce better clustering results than k-modes algorithm, the one related with our work most closely.
dc.description11 pages
dc.identifierhttps://arxiv.org/abs/cs/0509033
dc.identifierhttp://arxiv.org/abs/cs/0509033
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32939
dc.subjectArtificial Intelligence
dc.titleK-Histograms: An Efficient Clustering Algorithm for Categorical Dataset
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