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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Pattern recognition Jg. 45; H. 6; S. 2251 - 2265
Hauptverfasser:	Liang, Jiye, Zhao, Xingwang, Li, Deyu, Cao, Fuyuan, Dang, Chuangyin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.06.2012 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!