基于自适应状态连续性的多元时间序列稀疏逆协方差聚类

Lei Li, Wei Li, Jianxing Liao, Xuegang Hu
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引用次数: 3

摘要

与单变量时间序列聚类相比,多变量时间序列聚类已成为时间序列数据挖掘领域一个具有挑战性的研究课题。本文提出了一种基于模型的基于自适应状态连续性的稀疏逆协方差聚类方法(ASCSICC)。具体来说,引入状态连续性使传统的高斯混合模型(GMM)适用于时间序列聚类。为了防止过拟合,采用乘法器的交替方向法(ADMM)对GMM逆协方差参数进行优化。此外,该方法同时对时间序列进行分段和聚类。技术上,首先根据相邻时间序列数据的距离相似度估计自适应状态连续性;然后,采用自适应状态连续性聚类分配的动态规划算法作为e步,采用求解稀疏反协方差的ADMM算法作为m步。将e步和m步结合到期望最大化(EM)算法中进行聚类。最后,我们通过将ASC-SICC与几种最先进的方法在两个实际应用数据集上的实验中进行比较,证明了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive State Continuity-Based Sparse Inverse Covariance Clustering for Multivariate Time Series
Compared with univariate time series clustering, multivariate time series (MTS) clustering has become a challenging research topic on the data mining of time series. In this paper, a novel model-based approach Adaptive State Continuity-Based Sparse Inverse Covariance Clustering (ASCSICC) is proposed for MTS clustering. Specifically, the state continuity is introduced to make the traditional Gaussian mixture model (GMM) applicable to time series clustering. To prevent overfitting, the alternating direction method of multipliers (ADMM) is applied to optimize the parameter of GMM inverse covariance. In addition, the proposed approach simultaneously segments and clusters the time series. Technically, first, the adaptive state continuity is estimated based on the distance similarity of adjacent time series data. Then, a dynamic programming algorithm of cluster assignment by adaptive state continuity is taken as the E-step, and the ADMM for solving sparse inverse covariance is taken as the M-step. E-step and M-step are combined into an Expectation-Maximization (EM) algorithm to conduct the clustering process. Finally, we show the effectiveness of the proposed approach by comparing the ASC-SICC with several state-of-the-art approaches in experiments on two datasets from real applications.
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