A novel density-based clustering algorithm using nearest neighbor graph

•Nearest neighbor graph can indicate the samples that lying within the local dense regions of dataset without any input parameter.•A clustering algorithm named ADBSCAN is developed based on the nearest neighbor graph properties.•Experiments on different types of datasets demonstrate the superior per...

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Vydáno v:Pattern recognition Ročník 102; s. 107206
Hlavní autoři: Li, Hao, Liu, Xiaojie, Li, Tao, Gan, Rundong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2020
Témata:
DBSCAN
Density-based clustering
Nearest neighbor graph
DBSCAN
Density-based clustering
Nearest neighbor graph
ISSN:0031-3203, 1873-5142
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Shrnutí:•Nearest neighbor graph can indicate the samples that lying within the local dense regions of dataset without any input parameter.•A clustering algorithm named ADBSCAN is developed based on the nearest neighbor graph properties.•Experiments on different types of datasets demonstrate the superior performance and the robust to parameters of ADBSCAN. Density-based clustering has several desirable properties, such as the abilities to handle and identify noise samples, discover clusters of arbitrary shapes, and automatically discover of the number of clusters. Identifying the core samples within the dense regions of a dataset is a significant step of the density-based clustering algorithm. Unlike many other algorithms that estimate the density of each samples using different kinds of density estimators and then choose core samples based on a threshold, in this paper, we present a novel approach for identifying local high-density samples utilizing the inherent properties of the nearest neighbor graph (NNG). After using the density estimator to filter noise samples, the proposed algorithm ADBSCAN in which “A” stands for “Adaptive” performs a DBSCAN-like clustering process. The experimental results on artificial and real-world datasets have demonstrated the significant performance improvement over existing density-based clustering algorithms.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2020.107206