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Xiaowei Xu
Researcher at University of Arkansas at Little Rock
Publications - 167
Citations - 40082
Xiaowei Xu is an academic researcher from University of Arkansas at Little Rock. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 33, co-authored 125 publications receiving 36107 citations. Previous affiliations of Xiaowei Xu include Shenyang Ligong University & Ludwig Maximilian University of Munich.
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Journal ArticleDOI
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
TL;DR: The generalized algorithm DBSCAN can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes, and four applications using 2D points (astronomy, 3D points,biology, 5D points and 2D polygons) are presented, demonstrating the applicability of GDBSCAN to real-world problems.
Journal ArticleDOI
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
TL;DR: In new experiments, it is shown that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest.
Proceedings ArticleDOI
SCAN: a structural clustering algorithm for networks
TL;DR: A novel algorithm called SCAN (Structural Clustering Algorithm for Networks), which detects clusters, hubs and outliers in networks and clusters vertices based on a structural similarity measure is proposed.