Abstract
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.
Keywords
- Machine Learning: Time-series;Data Streams
- Machine Learning: Clustering
- Machine Learning: Data Mining
