Density Based Clustering (DBC) methods are capable of identifying arbitrary shaped data clusters in the presence of noise. DBC methods are based on the notion of local neighborhood density estimation. A major drawback of DBC methods is their poor performance in high-dimensions. In this work, a novel DBC method that performs well in high-dimensions is presented. The novelty of the proposed method can be summed up as follows: a hybrid first-second order optimization algorithm for identifying high-density data points; an adaptive scan radius for identifying reachable points. Theoretical results on the validity of the proposed method are presented in this work. The effectiveness and efficiency of the proposed approach are illustrated via rigorous experimental evaluations. The proposed method is compared with the well known DBC methods on synthetic and real data from the literature. Both internal and external cluster validation measures are used to evaluate the performance of the proposed method.

基于密度的聚类 (DBC) 方法能够在存在噪声的情况下识别任意形状的数据聚类。DBC 方法基于局部邻域密度估计的概念。DBC 方法的一个主要缺点是它们在高维上的性能不佳。在这项工作中，提出了一种在高维度上表现良好的新型 DBC 方法。所提出方法的新颖性可以概括如下：一种用于识别高密度数据点的混合一二阶优化算法；用于识别可达点的自适应扫描半径。在这项工作中提出了关于所提出方法有效性的理论结果。通过严格的实验评估说明了所提出方法的有效性和效率。将所提出的方法与文献中关于合成和真实数据的众所周知的DBC方法进行了比较。内部和外部集群验证措施都用于评估所提出方法的性能。