Title :
An improved ant colony clustering algorithm
Author :
Jiang, Hong ; Yu, Qingsong ; Gong, Yu
Author_Institution :
Comput. Center, East China Normal Univ., Shanghai, China
Abstract :
Based on the basic model of ant colony clustering algorithm, LF, an improved ant colony clustering algorithm (IACC) is proposed. The constructing method, the colony similarity, and the behavior of the ant are redefined. A new adaptive parameter adjustment strategy is also presented in this paper. Experimental results on clustering benchmarks indicate that the proposed algorithm has better performance than LF. It overcomes LF´s shortcomings of lower convergence speed and longer iteration cycles.
Keywords :
biocybernetics; data mining; particle swarm optimisation; pattern clustering; physiological models; ant colony clustering algorithm; ant colony optimization; artificial swarm intelligence; bionic algorithm; data mining; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Convergence; Data mining; Euclidean distance; Iris; LF algorithm; ant colony algorithm; ant colony clustering algorithm; clustering;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6495-1
DOI :
10.1109/BMEI.2010.5639719
