Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest

With increasing powering of data storage and advances in data generation and collection technologies, large volumes of time series data become available and the content is changing rapidly. This requires the data mining methods to have low time complexity to handle the huge and fast-changing data. This paper presents a novel time series clustering algorithm that has linear time complexity. The proposed algorithm partitions the data by checking some randomly selected symbolic patterns in the time series. Theoretical analysis is provided to show that group structures in the data can be revealed from this process. We evaluate the proposed algorithm extensively on all 85 datasets from the well-known UCR time series archive, and compare with the state-of-the-art approaches with statistical analysis. The results show that the proposed method is faster, and achieves better accuracy compared with other rival methods.