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

页码：410 / 413

页数：4

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