K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data

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dc.creatorHe, Zengyou
dc.creatorXu, Xiaofei
dc.creatorDeng, Shengchun
dc.date2005-11-03
dc.date.accessioned2026-07-07T06:49:33Z
dc.date.available2026-07-07T06:49:33Z
dc.descriptionClustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular k-means algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, Average Normalized Mutual Information-ANMI) borrowed from cluster ensemble. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-art categorical data clustering algorithms with respect to clustering accuracy.
dc.description18 pages
dc.identifierhttps://arxiv.org/abs/cs/0511013
dc.identifierhttp://arxiv.org/abs/cs/0511013
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/104385
dc.subjectArtificial Intelligence
dc.subjectDatabases
dc.titleK-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data
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