Title :
A Novel Ant Colony Optimization Algorithm for Clustering
Author :
Zhang, Xin ; Peng, Hong ; Zheng, Qilun
Abstract :
Clustering analysis is one kind of pattern recognition that not to be supervised. The clustering algorithm based on object function resolves the clustering problem into optimization problem, thereby it becomes to the main investigatory stream nowadays. But it has some shortcomings such as its sensitivity to initial condition, and it is easy to fall in local peak. To overcome these deficiencies, ant colony optimization algorithm is applied to clustering analysis and a novel clustering based on an improved ant colony optimization algorithm is proposed. Theoretical analysis and experiments show this method is faster and more efficient to convergence upon the optimal value in the whole field
Keywords :
optimisation; pattern clustering; ant colony optimization algorithm; clustering analysis; Agricultural engineering; Algorithm design and analysis; Ant colony optimization; Cities and towns; Clustering algorithms; Computer science; Educational institutions; Particle tracking; Pattern analysis; Pattern recognition;
Conference_Titel :
Signal Processing, 2006 8th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9736-3
Electronic_ISBN :
0-7803-9736-3
DOI :
10.1109/ICOSP.2006.345737