Graph theory with Modify-edge Clustering Algorithm Based on Maximum Weighted Entropy

Author

Li Lao ; Wu, Xiaoming ; Wu, Kai ; Zhu, Xuefeng

Author_Institution

Inst. of Biomech., South China Univ. of Technol., Guangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

9730

Lastpage

9733

Abstract

Combining with the graph theory clustering methods, an entropy-objective function algorithm was proposed for clustering. The edge which connects the vertices in non-orientation graph was redefined according to the distribution and distance of the data set. The objective function of the weighted entropy based on intra-variance in cluster and variance between clusters was built. The cluster result for the data set is derived from the maximum objective function. This algorithm doesn´t need the prior knowledge about the cluster number and the initialization centre

Keywords

graph theory; maximum entropy methods; pattern clustering; cluster intravariance; clustering analysis; entropy-objective function algorithm; graph theory clustering; maximum weighted entropy; modify-edge clustering algorithm; nonorientation graph; Analysis of variance; Automation; Biomechanics; Clustering algorithms; Clustering methods; Educational institutions; Entropy; Graph theory; Information science; Intelligent control; Clustering analysis; graph theory; intra-variance in cluster; variance between cluster; weighted entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713893

Filename

1713893