Affiliations: School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China
Correspondence:
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Corresponding author: Peng Pu, School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China. E-mail: ppu@cc.ecnu.edu.cn.
Abstract: Time series clustering has been attracted great interest in the last decade. Most time series clustering works focus on clustering algorithms and similarity measures. Recently a u-shapelet-based time series clustering method has been proposed which can not only hold a high performance of clustering but also offer an acceptable interpretation of clustering result. Time complexity is the major issue in the process of discovering u-shapelets, particularly for huge datasets. In this paper, we propose a Random Local Search algorithm that reduces the time to discover u-shapelets, meanwhile keeps or even improves the quality of clustering. Our algorithm first randomly samples subsequences from time series to reduce the time cost of the u-shapelet search problem. It then uses a local search strategy to make clustering result more excellent and stable. We test our approach extensively on 27 UCR time series datasets, and obtain improved clustering accuracies over existing approaches. The experiment shows that our method outperforms the primitive algorithm by up to 2 orders of magnitude on some datasets in runtime. Because the method allows a fast u-shapelet discovery, it is feasible to apply u-shapelets clustering on large datasets.
Keywords: Data mining, time series clustering, u-shapelet, random algorithm, local search method
