Online Segmentation Algorithm for Time Series Based on BIRCH Clustering Features

Online time series data representation is one of important problems of time series data mining. The adjacent points of time series are inherently depended and hence have similar clustering features. Based on BIRCH clustering features, we present a new kind of OSBC algorithm for time series segmentation in this paper. Using cluster features, OSBC algorithm can find easily the changing patterns of time series and achieve better segmentation results. The time complexity of OSBC algorithm is linear and its space complexity is also much smaller. The experiment results on time series benchmark show the effectiveness of our method.