{"type":"ScholarlyArticle","title":"On Metric DBSCAN with Low Doubling Dimension","authors":[{"name":"Hu Ding","slug":"hu-ding","given":"Hu","family":"Ding","affiliation":"The School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Fan Yang","slug":"fan-yang","given":"Fan","family":"Yang","affiliation":"The School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Mingyue Wang","slug":"mingyue-wang","given":"Mingyue","family":"Wang","affiliation":"The School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"The density based clustering method Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method for outlier recognition and has received tremendous attention from many different areas. A major issue of the original DBSCAN is that the time complexity could be as large as quadratic. Most of existing DBSCAN algorithms focus on developing efficient index structures to speed up the procedure in low-dimensional Euclidean space. However, the research of DBSCAN in high-dimensional Euclidean space or general metric spaces is still quite limited, to the best of our knowledge. In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension. We apply a novel randomized k-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBSCAN procedure. Our proposed algorithms do not need to build any complicated data structures and are easy to implement in practice. The experimental results show that our algorithms can significantly outperform the existing DBSCAN algorithms in terms of running time.","doi":"10.24963/ijcai.2020/426","url":"https://ijcai.org/proceedings/2020/426","pdf":"https://ijcai.org/proceedings/2020/0426.pdf","markdown":"https://ijcai.org/proceedings/2020/426.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/426","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":426,"pages":{"first":3080,"last":3086},"keywords":["Data Mining: Clustering, Unsupervised Learning","Machine Learning: Unsupervised Learning","Machine Learning: Clustering"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24233/file_24233.m3u8","length_sec":299,"provider":"hls"}],"references":[{"position":1,"raw":"[Andoni and Indyk, 2008] Alexandr Andoni and Piotr Indyk. 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