Unsupervised non-hierarchical entropy-based clustering

被引：0
|
作者：
Jardino, M [1 ]
机构：
[1] Lab Informat Mecan & Sci Ingenieur, F-91403 Orsay, France
来源：
DATA ANALYSIS, CLASSIFICATION, AND RELATED METHODS | 2000年
关键词：
D O I：
暂无
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
We present an unsupervised non-hierarchical clustering which realizes a partition of unlabelled objects in K non-overlapping clusters. The interest of this method rests on the convexity of the entropy-based clustering criterion which is demonstrated here. This criterion permits to reach an optimal partition independently of the initial conditions, with a step by step iterative Monte-Carlo process. Several data sets serve to illustrate the main properties of this clustering.
引用
收藏
页码：29 / 34
页数：6
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