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.

由于时间序列在各个领域的广泛存在，近年来对时间序列数据挖掘（尤其是时间序列聚类）的研究有所增加。聚类之类的技术可以从时间序列数据中提取有价值的信息和潜在的模式。在这方面，由于高维，对多元时间序列的聚类分析具有挑战性。我们的研究使我们开发了一种基于复杂网络的多元时间序列聚类（BCNC）的新方法。BCNC包括将多元时间序列映射到复杂网络中的新方法，以及使多元时间序列可视化的新方法。该解决方案创新性地基于关系网络，并依赖于社区检测技术的使用来实现完整的多元时间序列聚类。报道了所提出的BCNC方法的详细算法和仿真实验。在各种数据集上的实验结果表明，BCNC优于传统的多元时间序列聚类方法。