Clustering analysis of Dow Jones 30 based on extreme points

In this paper, Dow Jones 30 (DJ30) are clustered by emerging clustering method based on the differences of stocks´ synchronic extreme points´ emerging time and their implied range. This method can be applied to classify numerous and disordered data. During the clustering processes, Entropy Method is used to establish stock-distance by principal component. By linear programming method, we clustered DJ30, the results show that this method can cluster stocks with similar trend together: within clusters the curves of stocks are homogeneous and among clusters the curves of stocks are inhomogeneous.