title: A review of subsequence time series clustering.
abstract: Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
authors.affiliation: Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia.
authors.forename: Seyedjamal
authors.identifier: 0000-0002-7672-3040
authors.initials: S
authors.lastname: Zolhavarieh
authors.affiliation: Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia.
authors.forename: Saeed
authors.identifier: 0000-0003-1908-4227
authors.initials: S
authors.lastname: Aghabozorgi
authors.affiliation: Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia.
authors.forename: Ying Wah
authors.initials: YW
authors.lastname: Teh
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining; D008962:Models, Theoretical