Title :
Two-phase Incremental Clustering Algorithm Based on Immune Response and Ant Colony
Author :
Li, Xianghua ; Wang, Zhengxuan ; Chen, Shoukong
Author_Institution :
Dept. of Comput. Sci. & Technol., Jilin Univ., Zhuhai, China
Abstract :
It is a trend for paradigms of nature-inspired computing to hybrid. Inspired by the principle of immune response in the immune system, a novel incremental data clustering algorithm called IRA was proposed in previous work. It obtains high quality clustering. However, the number of clusters obtained by IRA is more than the actual ones. Therefore, the clustering algorithm based on ant colony called OAA is taken into account to optimize the results of IRA. Then, both IRA and OAA form a two-phase incremental clustering algorithm. The experimental results are presented and discussed which demonstrate acceptable accuracy couple with efficiency in running time and compression.
Keywords :
artificial immune systems; pattern clustering; IRA; OAA; ant colony; high quality clustering; immune response; immune system; incremental data clustering algorithm; nature-inspired computing; two-phase incremental clustering algorithm; Adaptation model; Algorithm design and analysis; Breast cancer; Clustering algorithms; Heuristic algorithms; Merging; Optimization; ant colony; immune response; incremental clustering; optimization;
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-8891-9
Electronic_ISBN :
978-0-7695-4281-2
DOI :
10.1109/ICGEC.2010.92
