基于蚁群聚类算法的优化与改进

传统的蚁群聚类算法将聚类数据的每一维属性都等同看待，而在实际的应用中各维属性对聚类的贡献率不一，具有主次之分，若将所有属性赋予相同的权重，将对聚类的效果造成影响．为了克服这个缺陷，本文将主成份分析（PCA）方法引入到蚁群聚类当中，利用PCA计算属性的贡献率并以此构建属性的权重．在此基础上，结合一个新的初始化策略，提出了一种属性带权的改进蚁群聚类算法．通过对多个UCI数据集的测试，验证了本算法的有效性．实验结果表明，合理的权重分配能够有效的提高蚁群聚类的质量．

Optimization and Improvement Based on Ant Colony Clustering Algorithm

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.