New information-based clustering method using Renyi's entropy and fuzzy C-means clustering

被引：0
|
作者：
Aghagolzadeh, M [1 ]
Soltanian-Zadeh, H [1 ]
Araabi, B [1 ]
机构：
[1] Univ Tehran, Dept Elect & Comp Engn, Tehran 14395, Iran
来源：
SEVENTH IASTED INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING | 2005年
关键词：
information theory; Renyi's entropy; top-down hierarchical algorithms; clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new clustering method based on Renyi entropy. The proposed method maximizes entropy of clusters using between and within clusters entropies. It is a top-down multi-resolution method and uses the initial clusters found by Fuzzy C-Means. Applications of the proposed algorithm on the synthetic data are compared with those of C-Means and Gustafson-Kessel algorithms. Results show superiority of the proposed algorithm to these methods.
引用
收藏
页码：410 / 413
页数：4
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