Abstract
A novel adaptive ant colony clustering algorithm based on digraph (A3CD) is presented. Inspired by the swarm intelligence shown through the social insects' self-organizing behavior, in A3CD we assign acceptance weights on the directed edges of a pheromone digraph. The weights of the digraph is adaptively updated by the pheromone left by ants in the seeking process. Finally, strong connected components are extracted as clusters under a certain threshold. A3CD has been implemented and tested on several clustering benchmarks and real datasets to compare the performance with the classical K-means clustering algorithm and LF algorithm which is also based on ACO. Experimental results show that our algorithm is easier to implement, more efficient and performs faster and has better clustering quality than other methods.
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