A new clustering algorithm

Author

Yang, Xinbin ; Huang, Dao

Author_Institution

Comput. Dept., DongYing Vocational Coll., Dong Ying, China

Volume

5

fYear

2004

Firstpage

4278

Abstract

Clustering analysis is an important part of the data mining community. Traditional clustering algorithm is slow in convergence and sensitive to the initial value and preset classed in large scale data set. Ant colony algorithm is a kind of evolutionary algorithm with global optimization quality to deal with discrete problems. The ant colony algorithm is applied in aggregation analysis for the first time in this paper. A new clustering algorithm is presented based on the ant colony algorithm. This algorithm has the qualities of essential parallel, quick convergence and high effectiveness. The experimental result shows that it is about 10% higher than the C-means method in effectiveness.

Keywords

convergence; data mining; evolutionary computation; optimisation; pattern clustering; statistical analysis; C-means method; aggregation analysis; ant colony algorithm; clustering algorithm; clustering analysis; convergence; data mining; discrete problems; evolutionary algorithm; large scale data set; optimization; Algorithm design and analysis; Ant colony optimization; Clustering algorithms; Convergence; Data mining; Educational institutions; Electronic mail; Evolutionary computation; Information analysis; Large-scale systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1342318

Filename

1342318