Time series clustering based on complex network with synchronous matching states. (January 2023)

• Record Type: Journal Article
• Authors: Li, Hailin; Liu, Zechen; Wan, Xiaoji
• Is Part Of: Expert systems with applications. Volume 211(2023)
• Publication Date: 2023-01
• Subjects: Time series clustering -- Complex network -- Synchronous matching -- Data mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118543
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Due to the extensive existence of time series in various fields, more and more research on time series data mining, especially time series clustering, has been done in recent years. Clustering technology can extract valuable information and potential patterns from time series data. This paper proposes a time series Clustering method based on Synchronous matching of Complex networks (CSC). This method uses density peak clustering algorithm to identify the state of each time point and obtains the state sequence according to the timeline of the original time series. State sequences is a new method to represent time series. By comparing two state sequences synchronously, the length of state sequence with step is calculated and the similarity is presented, which forms a new method to calculate the similarity of time series. Based on the obtained time series similarity, the relationship network of time series is constructed. Simultaneously, the community discovery technology is applied to cluster the relationship network and further achieve the complete time series clustering. The detailed process and simulation experiments of CSC method are given. Experimental results on different datasets show that CSC method is superior to other traditional time series clustering methods. Highlights: The constructed state sequence can describe the time series more effectively. Time series similarity measure method is powerful and time complexity is low. The proposed method is suitable for clustering multiple time series data. It does not require to manually set the number of communities.