LB HUST: A Symmetrical Boundary Distance for Clustering Time Series
Junkui Li, Wang Yuanzhen, Xinping Li · 2006
Clustering is an important technology in mining time series, and the key is to define the similarity or dissimilarity between data. One of existing time series distance measures LB_Keogh, is tighter lower bounding than Euclidean and dynamic time warping (DTW), however, it is an asymmetrical distance measure, and has its limitation in clustering.To solve the problem, we present a symmetrical boundary distance measure called LB_HUST, and prove that it is tighter lower bounding than LB_Keogh. We apply LB_HUST to cluster time series, and update the boundary of the cluster when a new time series is added into the cluster. The experiments show that the method exceeds the approaches based on Euclidean and DTW in terms of accuracy.