Title :
Genetic Clustering Algorithm Based on Dynamic Granularity
Author :
Zhen, Jia ; Gui, Wang Yong
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Tech. Univ., Huludao, China
Abstract :
From the view of granularity, this paper presents a genetic clustering algorithm based on dynamic granularity. In view of a parallel, random search, global optimization and diversity characteristics of genetic algorithm, it is combined with dynamic granularity model. In the process of granularity changing, appropriate granulation can be made by coarsening and refining the granularity, which can ensure clustering efficiency and quality of the algorithm. Experimental data show that the method effectively improves the clustering algorithm based on genetic algorithm local search ability and convergence speed.
Keywords :
genetic algorithms; pattern clustering; search problems; diversity characteristics; dynamic granularity; genetic clustering algorithm; granularity changing process; granularity coarsening; granularity refining; Clustering algorithms; Clustering methods; Concurrent computing; Genetic algorithms; Genetic engineering; Heuristic algorithms; Industrial electronics; Industrial engineering; Machine learning algorithms; Optimization methods; K-medoids algorithm; dynamic granularity; genetic algorithm; harmony;
Conference_Titel :
Computing, Control and Industrial Engineering (CCIE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-4026-9
DOI :
10.1109/CCIE.2010.114