Title :
An Ant Colony Clustering Algorithm
Author_Institution :
Mudanjing Teachers Coll., Mudanjing
Abstract :
This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. The algorithm employs the global pheromone updating and the heuristic information to construct clustering solutions and uniform crossover operator to further improve solutions discovered by ants. This algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with other popular heuristic methods. Our computational simulations reveal very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required.
Keywords :
optimisation; pattern clustering; ant colony clustering algorithm; global pheromone updating; optimization; uniform crossover operator; Ant colony optimization; Clustering algorithms; Computational modeling; Cybernetics; Educational institutions; Legged locomotion; Machine learning; Machine learning algorithms; Mathematics; Partitioning algorithms; Ant colony algorithm; Clustering; Optimization; Uniform crossover;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370833
