Abstract:

For solving the problem that traditional clustering algorithms can not get good results on clustering of complex data sets, a clustering algorithm based on the ant colony system is presented. The ant’s random search is realized based on the principle that the ant leaves pheromone on its path and pursuit concentrated pheromone.  The similarity among the data is determined based on the concept of neighboring function values.  Ants establish  connections between the most similar data in the process of walking,  draw the various sub-connected graph,  and the data in the same subconnected graph are given the same cluster number. Some experiments have been made to compare the results of the proposed CAACS algorithm with those  of the DBSCAN algorithm. The experiments are based on randomly generated irregular data sets and a series of synthetic data sets. The experimental results show that the CAACS algorithm can achieve good results on complex data sets.
 

Key words: clustering algorithm, ant colony system, density-based spatial clustering of application with noise