PSO algorithm with stochastic inertia weight and its application in clustering

Author

Chen, Jili

Author_Institution

Coll. of Inf. Sci. & Eng., Guilin Univ. of Technol., Guilin, China

Volume

2

fYear

2011

fDate

9-11 Dec. 2011

Firstpage

59

Lastpage

62

Abstract

PSO algorithm with stochastic inertia weight has better converging speed and ability than the basic PSO algorithm. The PSO algorithm with stochastic inertia is analyzed, and is applied to the clustering algorithm. The data sets of UCI data collection are used to experiment, the results of the experiment shows that the new clustering algorithm is better than K-means algorithm in quantization error, and the result of clustering is not affected by the size of the particle swarm. The application in instruction websites of the new clustering algorithm is discussed.

Keywords

data mining; particle swarm optimisation; pattern clustering; K-means algorithm; PSO algorithm; UCI data collection; clustering algorithm; data mining; instruction Websites; particle swarm optimisation; stochastic inertia weight; Algorithm design and analysis; Clustering algorithms; Data mining; Education; Heuristic algorithms; Ionosphere; Particle swarm optimization; Cluster; Particle swarm optimization; Stochastic inertia weight;

fLanguage

English

Publisher

ieee

Conference_Titel

IT in Medicine and Education (ITME), 2011 International Symposium on

Conference_Location

Cuangzhou

Print_ISBN

978-1-61284-701-6

Type

conf

DOI

10.1109/ITiME.2011.6132057

Filename

6132057