IEEE Trans Cybern. 2021 Dec;51(12):6253-6261. doi: 10.1109/TCYB.2020.2970455. Epub 2021 Dec 22.
In this article, we propose a two-stage time-series clustering approach to cluster time series with different shapes. The first step is to represent the time series by a suite of information granules following the principle of justifiable granularity to perform dimensionality reduction, while the second step is to realize the fuzzy clustering of the time series in the transformed representation space (viz., the space of information granules). In the dimensionality reduction process, the numerical data are granulated using a collection of information granules forming a new sequence that can well describe the original time series. Then, when clustering the time series, dynamic time warping (DTW) is employed to measure the similarity between time series and DTW barycenter averaging (DBA) is generalized to weighted DBA to be involved in the fuzzy C -means (FCMs) algorithm. Finally, the experiments are conducted on the datasets coming from UCR time-series database and Chinese stocks to demonstrate the effectiveness and advantages of the proposed fuzzy clustering approach.
在本文中，我们提出了一种两阶段时间序列聚类方法，用于聚类形状不同的时间序列。第一步是通过一系列符合合理粒度原则的信息粒来表示时间序列，以进行降维，而第二步是在转换后的表示空间（即信息粒空间）中实现时间序列的模糊聚类。在降维过程中，使用一组信息粒对数值数据进行粒度划分，形成一个新的序列，可以很好地描述原始时间序列。然后，在聚类时间序列时，采用动态时间规整（DTW）来测量时间序列之间的相似性，并将 DTW 重心平均（DBA）推广到加权 DBA，以参与模糊 C-均值（FCM）算法。最后，在 UCR 时间序列数据库和中国股票数据集上进行实验，以验证所提出的模糊聚类方法的有效性和优势。
