Title :
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
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;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.251
