Dynamic clustering algorithm for time series
一种时间序列动态聚类的算法

This paper proposed a dynamic clustering algorithm for time series aiming at solving the shortcoming of traditional static clustering. Firstly, the method extracted the key point set of each time series, and then obtained the dynamic time series by using improved FCM algorithm. At last, detected the cluster of dynamic time series which belonged to each time segment based on the dynamic clustering algorithm. The adoption of L-W distance in FCM algorithm could avoid the shortcoming of sensitivity to singular value. The experimental results obtained by the proposal reflect the evolutional property that the clusters of the dynamic time series change over time, and show the validity and the feasibility of the method. Compared with existed algorithms, the proposed algorithm indicates the dynamic characteristic of time series when clustering them. This algorithm can also be applied to other problems in data mining.