Title :
A hybrid genetic based clustering algorithm
Author :
Liu, Yong-Guo ; Chen, Ke-Fei ; Li, Xue-Ming
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China
Abstract :
A hybrid genetic based clustering algorithm, called HGA-clustering, is proposed in this article to explore the proper clustering of data sets. The presented algorithm, with the cooperation of tabu list and aspiration criteria, can achieve harmony between population diversity and convergence speed. Its superiority over K-means algorithm and another genetic algorithm based clustering approach is extensively demonstrated for artificial and real life data sets.
Keywords :
genetic algorithms; pattern clustering; search problems; HGA clustering; K-means algorithm; artificial life data sets; aspiration criteria; convergence speed; hybrid genetic based clustering algorithm; population diversity; real life data sets; tabu list; Algorithm design and analysis; Clustering algorithms; Computer science; Convergence; Data engineering; Genetic algorithms; Genetic engineering; Hybrid integrated circuits; Iterative algorithms; Partitioning algorithms;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382045
