Title :
A new hybrid learning-based algorithm for data clustering
Author :
Khoshdel, Hamed ; Saman, Barat
Author_Institution :
Dept. of Comput. Eng., Islamic Azad Univ., Shirvan, Iran
Abstract :
In this paper a new hybrid algorithm based on particle swarm optimization (PSO), k-means and learning automata (KPSOLA) is proposed for data clustering. In the proposed algorithm, learning automata acts as the thinking brain of the particles in PSO. In each of iterations of the proposed algorithm execution, corresponding learning automata of each particle decides whether next move of that particle to be with respect to PSO algorithm or with respect to k-means algorithm. The proposed algorithm and also 4 other clustering algorithms have been used for clustering 6 standard datasets and their efficiencies are compared with each other. Experimental results show that the proposed algorithm has an acceptable efficiency and robustness.
Keywords :
iterative methods; learning (artificial intelligence); learning automata; particle swarm optimisation; pattern clustering; KPSOLA; PSO; data clustering; hybrid learning-based algorithm; iterations; k-means algorithm; k-means and learning automata; particle swarm optimization; Algorithm design and analysis; Clustering algorithms; Convergence; Learning automata; Particle swarm optimization; Signal processing algorithms; Vectors; Data clustering; k-means; learning automata; particle swarm optimization;
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on
Conference_Location :
Shiraz, Fars
Print_ISBN :
978-1-4673-1478-7
DOI :
10.1109/AISP.2012.6313725