Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks

	Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks
	Lihua Zhou; Guowang Du; Ruxin Wang; Dapeng Tao; Lizhen Wang; Cheng Jun; Jing Wang
刊名	IEEE ACCESS
	2018
文献子类	期刊论文
英文摘要	In multivariate time series clustering, the inter-similarity across distinct variates and the intra-similarity within each variate pose analytical challenges. Here we propose a novel multivariate time series clustering method using multi-nonnegative matrix factorization (MNMF) in multi-relational networks. Specifically, a set of multivariate time series is transformed from the time-space domain into a multi-relational network in the topological domain. Then, the multi-relational network is factorized to identify time series clusters. The transformation from the time-space domain to the topological domain benefits from the ability of networks to characterize both local and global relationships between nodes, and MNMF incorporates inter-similarity across distinct variates into clustering. Furthermore, to trace the evolutionary trends of clusters, time series are transformed into a dynamic multi-relational network, thereby extending MNMF to dynamic MNMF. Extensive experiments illustrate the superiority of our approach compared with current state-of-the-art algorithms.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13590]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Lihua Zhou,Guowang Du,Ruxin Wang,et al. Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks[J]. IEEE ACCESS,2018.
APA	Lihua Zhou.,Guowang Du.,Ruxin Wang.,Dapeng Tao.,Lizhen Wang.,...&Jing Wang.(2018).Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks.IEEE ACCESS.
MLA	Lihua Zhou,et al."Clustering multivariate time series data via multi-nonnegative matrix factorization in multi-relational networks".IEEE ACCESS (2018).