Two Novel Swarm Intelligence Clustering Analysis Methods

Author

Zhou, Yongquan ; Liu, Bai

Author_Institution

Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Manning, China

Volume

4

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

497

Lastpage

501

Abstract

Clustering analysis is one of the primary techniques in the field of data mining. It is an unsupervised mode of pattern recognition. Clustering analysis is a division of data into similarity groups according to the given rules. In this paper, two novel swarm intelligence clustering analysis methods base on artificial fish-school algorithm and population migration algorithm are proposed. The results of the experiments show that the two new swarm intelligence clustering analysis methods can efficiently and accurately classify class data.

Keywords

data mining; optimisation; pattern clustering; artificial fish-school algorithm; data mining; pattern recognition; population migration algorithm; swarm intelligence clustering analysis methods; Algorithm design and analysis; Clustering algorithms; Concrete; Data mining; Educational institutions; Humans; Marine animals; Particle swarm optimization; Pattern recognition; Protection; Artificial fish-school algorithm; K-means method; clustering analysis; population migration algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.251

Filename

5363228