DocumentCode :
2772651
Title :
Particle Swarm Optimization Based Hierarchical Agglomerative Clustering
Author :
Alam, Shafiq ; Dobbie, Gillian ; Riddle, Patricia ; Naeem, M. Asif
Author_Institution :
Dept. of Comput. Sci., Univ. of Auckland, Auckland, New Zealand
Volume :
2
fYear :
2010
fDate :
Aug. 31 2010-Sept. 3 2010
Firstpage :
64
Lastpage :
68
Abstract :
Clustering- an important data mining task, which groups the data on the basis of similarities among the data, can be divided into two broad categories, partitional clustering and hierarchal. We combine these two methods and propose a novel clustering algorithm called Hierarchical Particle Swarm Optimization (HPSO) data clustering. The proposed algorithm exploits the swarm intelligence of cooperating agents in a decentralized environment. The experimental results were compared with benchmark clustering techniques, which include K-means, PSO clustering, Hierarchical Agglomerative clustering (HAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results are evidence of the effectiveness of Swarm based clustering and the capability to perform clustering in a hierarchical agglomerative manner.
Keywords :
data mining; particle swarm optimisation; pattern clustering; DBSCAN; HAC; HPSO; PSO clustering; benchmark clustering techniques; cooperating agents; data clustering; data mining; decentralized environment; density-based spatial clustering of applications with noise; hierarchical agglomerative clustering; hierarchical particle swarm optimization; k-mean clustering algorithm; partitional clustering; swarm based clustering; swarm intelligence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on
Conference_Location :
Toronto, ON
Print_ISBN :
978-1-4244-8482-9
Electronic_ISBN :
978-0-7695-4191-4
Type :
conf
DOI :
10.1109/WI-IAT.2010.75
Filename :
5616434
Link To Document :
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