Abstract:The traditional ant colony clustering algorithm treats all features of data set equally. But in practice, the contribution rate of attributes is different from each other. Therefore, giving all features the same weight will eventually affect the quality of clustering. To overcome the defect, the method of principal components analysis is introduced into the ant colony clustering algorithm to calculate the contribution rates of attributes and to construct the weights of attributes. On this basis, combined with a new initialization strategy, an improved ant colony algorithm with weighted attributes is proposed in this paper. The experiments on several UCI data sets validated the effectiveness of the proposed algorithm. The results show that reasonable weight distribution can effectively improve the quality of clustering.