Multivariate time series clustering based on complex network. (July 2021) - Li, Hailin; Liu, Zechen
Highlights: The constructed networks improve the accuracy of the clustering results. The network features reflect the information and reduce the dimension of MTS. The proposed method is suitable for large MTS data clustering. It noes not require to manually set the number of communities. Abstract: Recent years have seen an increase in research on time series data mining (especially time-series clustering) owing to the widespread existence of time series in various fields. Techniques such as clustering can extract valuable information and potential patterns from time-series data. In this regard, the clustering analysis of multivariate time series is challenging because of the high dimensionality. Our study led us to develop a novel method based on complex networks for multivariate time series clustering (BCNC). BCNC includes a new method for mapping multivariate time series into complex networks and a new method to visualize multivariate time series. The solution is innovatively based on a relationship network and relies on the use of community detection technology to achieve complete multivariate time series clustering. The detailed algorithm and the simulation experiments of the proposed BCNC method are reported. The experimental results on various datasets show that BCNC is superior to traditional multivariate time series clustering methods.