Online Segmentation Algorithm for Time Series Based on Hierarchical Clustering
DU Yi, LU DeTang , LI DaoLun, ZHA WenShu
Institute of Engineering and Science Software, University of Science and Technology of China, Hefei 230027 Key Laboratory of Software in Computing and Communication of Anhui Province, Hefei 230027
Abstract How to segment sequential data in realtime is becoming one of the most important tasks in the time series mining domain. A new online segmentation algorithm called online segmentation algorithm for time series based on hierarchical clustering (OSHC) is presented. According to the order characteristics of sequence data, a novel Segment Feature List (SFList) is developed to save segmentation information. In the algorithm, time series are segmented effectively with one scan of the database and the time complexity is O(n). Historical information can also be inquired quickly by using the SFList. Experimental results show that the algorithm is efficient.
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2007, Vol. 20 
