基于成对约束的SubKMeans聚类数确定算法

随着数据维度的增加,传统聚类算法会出现聚类性能差的现象.SubKMeans是一种功能强大的子空间聚类算法,旨在为K-Means类算法搜索出一个最佳子空间,降低高维度影响,但是该算法需要用户事先指定聚类数目K值,而在实际使用中有时无法给出准确的K值.针对这一问题,引入成对约束,将成对约束与轮廓系数进行结合,提出了一种基于成对约束的SubKMeans聚类数确定算法.改进后的轮廓系数能够更加准确的评价聚类性能,从而实现K值确定,实验结果证明该方法的有效性.

SubKMeans Algorithm for Determining Number of Clusters Based on Pairwise Constraints

With the increase of data dimension, the traditional clustering algorithm will have poor clustering performance. SubKMeans is a powerful subspace clustering algorithm, which aims to search the best subspace for K-Means algorithm and reduce the impact of high dimensions. However, the algorithm requires users to specify the number of clusters K value in advance, and sometimes it can not give accurate K value in actual use. In order to solve this problem, the pairwise constraint is introduced, which is combined with the silhouette coefficient. A SubKMeans algorithm for determining the number of clusters based on the pairwise constraint is proposed. The improved silhouette coefficient can evaluate the clustering performance more accurately, so that the K value can be determined. The experimental results proves the effectiveness of the proposed method.