Determining the number of clusters using information entropy for mixed data

DOI10.1016/j.patcog.2011.12.017zbMath1234.68343OpenAlexW2165187708MaRDI QIDQ408047

Chuangyin Dang, Fuyuan Cao, Xingwang Zhao, De-Yu Li, J. Y. Liang

Publication date: 28 March 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.12.017

zbMATH Keywords

clusteringinformation entropymixed datanumber of clusters\(k\)-prototypes algorithmcluster validity index

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Fuzzy clustering of mixed data, Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy, Distance Metrics and Clustering Methods for Mixed‐type Data, Clustering mixed numerical and categorical data with missing values, Partitional clustering via successive transportation problems, Multigranulation rough sets: from partition to covering, Initialization of \(K\)-modes clustering using outlier detection techniques, Estimating the number of clusters in a ranking data context, A novel fast heuristic to handle large-scale shape clustering, A sequential ensemble clusterings generation algorithm for mixed data

Uses Software

• UCI-ml
• clusfind
• COOLCAT
• ROCK
• Cobweb/3

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