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...

Full description

Saved in:

Bibliographic Details
Published in:	Pattern recognition Vol. 45; no. 6; pp. 2251 - 2265
Main Authors:	Liang, Jiye, Zhao, Xingwang, Li, Deyu, Cao, Fuyuan, Dang, Chuangyin
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.06.2012 Elsevier
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!