Multivariate time-series (MTS) clustering is a fundamental technique in data mining with a wide range of real-world applications. To date, though some approaches have been developed, they suffer from various drawbacks, such as high computational cost or loss of information. Most existing approaches are single-view methods without considering the benefits of mutual-support multiple views. Moreover, due to its data structure, MTS data cannot be handled well by most multiview clustering methods. Toward this end, we propose a consistent and specific non-negative matrix factorization-based multiview clustering (CSMVC) method for MTS clustering. The proposed method constructs a multilayer graph to represent the original MTS data and generates multiple views with a subspace technique. The obtained multiview data are processed through a novel non-negative matrix factorization (NMF) method, which can explore the view-consistent and view-specific information simultaneously. Furthermore, an alternating optimization scheme is proposed to solve the corresponding optimization problem. We conduct extensive experiments on 13 benchmark datasets and the results demonstrate the superiority of our proposed method against other state-of-the-art algorithms under a wide range of evaluation metrics.

中文翻译：

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CSMVC：一种用于多元时间序列聚类的多视图方法


多元时间序列 (MTS) 聚类是数据挖掘的一项基本技术，具有广泛的实际应用。迄今为止，尽管已经开发了一些方法，但它们存在各种缺点，例如计算成本高或信息丢失。大多数现有方法都是单视图方法，没有考虑相互支持多视图的好处。此外，由于其数据结构，MTS数据不能被大多数多视图聚类方法很好地处理。为此，我们提出了一种一致且特定的基于非负矩阵分解的多视图聚类（CSMVC）方法用于 MTS 聚类。该方法构建了一个多层图来表示原始 MTS 数据，并使用子空间技术生成多个视图。获得的多视图数据通过一种新颖的非负矩阵分解（NMF）方法进行处理，该方法可以同时探索视图一致和视图特定的信息。此外，提出了交替优化方案来解决相应的优化问题。我们对 13 个基准数据集进行了广泛的实验，结果证明了我们提出的方法在各种评估指标下相对于其他最先进算法的优越性。