Title :
Ant Colony Clustering Algorithm Based on Swarm Intelligence
Author :
Dong Liyan ; Zhang Sainan ; Tian Geng ; Li Yongli ; Cai Guanyan
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Abstract :
Aim at the clustering result of traditional ant colony clustering algorithm is not accurate and the algorithm operating efficiency lower, many modified algorithm have been proposed. In this paper, we propose an ant colony clustering algorithm based on swarm intelligence. This algorithm not only improved from the method of calculating the similarity measure and enhanced ant memory, and also proposed a new policy of picking and dropping objects, which is picking the objects which have been formation of micro-clustering. Through experiment contrast, this paper presents the ant colony clustering algorithm based on swarm intelligence than the traditional ant colony algorithm in terms of efficiency, the correct rate of the clustering results have significantly improved.
Keywords :
ant colony optimisation; pattern clustering; swarm intelligence; ant colony clustering algorithm; microclustering; object dropping; object picking; similarity measure; swarm intelligence; Accuracy; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Euclidean distance; Machine learning algorithms; Particle swarm optimization; Ant Colony Clustering Algorithm; Data Mining; Micro-Cluster; Swarm Intelligence Algorithm;
Conference_Titel :
Intelligent Networks and Intelligent Systems (ICINIS), 2013 6th International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4799-2808-8
DOI :
10.1109/ICINIS.2013.38