Improved Ant Colony Clustering Algorithm Based on k-means Algorithm

The traditional k-means algorithm is a local search algorithm,which is sensitive to initialization and easy to search a local maximum. To solve this problem,an improved ant colony clustering algorithm based on k-means algorithm is proposed. The algorithm selects k-data objects which belong to high density area and are the furthest aw ay from each other as initial center,and introduces positive feedback,elitism mechanism and mutation operator into ant colony clustering algorithm. Experiments show that the algorithm has not only the weak dependence on initial data,but also higher clustering accuracy and fast convergence.