Context quantization based on the ant K-means clustering algorithm

In this paper, we present an improved context quantization algorithm based on a hybrid K-means and ant colony clustering algorithm. The K-means clustering algorithm is used to construct the initial solution for a context quantization problem. An ant colony based clustering algorithm is then used to improve the quality of the solution. During each iteration, objects are assigned to respective clusters based on the corresponding pheromone concentrations updated by the artificial ants. Then, a local search procedure is conducted by a small part of the ants with the best objective function values to further refine the solution. Experiment results show that the presented algorithm outperforms the K-means clustering based context quantization algorithm and the Maximum Mutual Information based context quantization algorithm under various quantization levels.