Abstract 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.

中文翻译：

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一种使用最近邻图的新的基于密度的聚类算法

摘要 基于密度的聚类有几个理想的特性，例如处理和识别噪声样本的能力，发现任意形状的集群以及自动发现集群数量的能力。识别数据集密集区域内的核心样本是基于密度的聚类算法的重要步骤。与使用不同类型的密度估计器来估计每个样本的密度然后根据阈值选择核心样本的许多其他算法不同，在本文中，我们提出了一种利用最近点的固有特性来识别局部高密度样本的新方法。邻居图（NNG）。在使用密度估计器过滤噪声样本后，所提出的算法 ADBSCAN（其中“A”代表“Adaptive”）执行类似 DBSCAN 的聚类过程。