{"type":"ScholarlyArticle","title":"Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest","authors":[{"name":"Xiaosheng Li","slug":"xiaosheng-li","given":"Xiaosheng","family":"Li","affiliation":"Department of Computer Science , George Mason University , USA"},{"name":"Jessica Lin","slug":"jessica-lin","given":"Jessica","family":"Lin","affiliation":"Department of Computer Science , George Mason University , USA"},{"name":"Liang Zhao","slug":"liang-zhao","given":"Liang","family":"Zhao","affiliation":"Department of Information Science and Technology , George Mason University , USA"}],"abstract":"With increasing powering of data storage and advances in data generation and\r\ncollection technologies, large volumes of time series data become available and\r\nthe content is changing rapidly. This requires the data mining methods to have\r\nlow time complexity to handle the huge and fast-changing data. This paper\r\npresents a novel time series clustering algorithm that has linear time\r\ncomplexity. The proposed algorithm partitions the data by checking some\r\nrandomly selected symbolic patterns in the time series. Theoretical analysis is\r\nprovided to show that group structures in the data can be revealed from this\r\nprocess. We evaluate the proposed algorithm extensively on all 85 datasets from\r\nthe well-known UCR time series archive, and compare with the state-of-the-art approaches\r\nwith statistical analysis. The results show that the proposed method is faster,\r\nand achieves better accuracy compared with other rival methods.","doi":"10.24963/ijcai.2019/406","url":"https://ijcai.org/proceedings/2019/406","pdf":"https://ijcai.org/proceedings/2019/0406.pdf","markdown":"https://ijcai.org/proceedings/2019/406.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/406","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":406,"pages":{"first":2930,"last":2936},"keywords":["Machine Learning: Clustering","Machine Learning: Data Mining","Machine Learning: Time-series;Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Aghabozorgi et al., 2015] Saeed Aghabozorgi, Ali Seyed Shirkhorshidi, and Teh Ying Wah. 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