Determining the number of clusters using information entropy for mixed data

In cluster analysis, one of the most challenging and difficult problems is the determination of the number of clusters in a data set, which is a basic input parameter for most clustering algorithms. To solve this problem, many algorithms have been proposed for either numerical or categorical data se...

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Vydáno v:	Pattern recognition Ročník 45; číslo 6; s. 2251 - 2265
Hlavní autoři:	Liang, Jiye, Zhao, Xingwang, Li, Deyu, Cao, Fuyuan, Dang, Chuangyin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.06.2012 Elsevier
Témata:	Algorithms Applied sciences Cluster validity index Clustering Clusters Complement Entropy (Information theory) Exact sciences and technology Information entropy Information, signal and communications theory k-Prototypes algorithm Mixed data Number of clusters Optimization Pattern recognition Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Cluster validity index k-Prototypes algorithm Number of clusters Clustering Information entropy Mixed data Cluster analysis Automatic classification Prototype Similarity Entropy Algorithm Signal classification Algorithm performance Numerical data Renyi theory
ISSN:	0031-3203, 1873-5142
On-line přístup:	Získat plný text
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