Title :
A new approach based on enhanced PSO with neighborhood search for data clustering
Author :
Dang Cong Tran ; Zhijian Wu ; Van Xuat Nguyen
Author_Institution :
State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China
Abstract :
The well-known K-means algorithm has been successfully applied to many practical clustering problems, but it has some drawbacks such as local optimal convergence and sensitivity to initial points. Particle swarm optimization algorithm (PSO) is one of the swarm intelligent algorithms, it is applied in solving global optimization problems. An integration of enhanced PSO and K-means algorithm is becoming one of the popular strategies for solving clustering problems. In this study, an approach based on PSO and K-means is presented (denoted EPSO), in which PSO is enhanced by neighborhood search strategies. By hybrid with enhanced PSO, it does not only help the algorithm escape from local optima but also overcomes the shortcoming of the slow convergence speed of the PSO algorithm. Experimental results on eight benchmark data sets show that the proposed approach outperforms some other data clustering algorithms, and has an acceptable efficiency and robustness.
Keywords :
data mining; particle swarm optimisation; pattern clustering; search problems; swarm intelligence; EPSO; K-means algorithm; data clustering; enhanced PSO; global optimization problem; neighborhood search; particle swarm optimization algorithm; swarm intelligent algorithm; Clustering algorithms; Convergence; Optimization; Search problems; Sociology; Statistics; Vectors; Data clustering; K-means; global optimization; neighborhood search; particle swarm optimization;
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2013 International Conference of
Conference_Location :
Hanoi
Print_ISBN :
978-1-4799-3399-0
DOI :
10.1109/SOCPAR.2013.7054109