Local outlier detection is a hot area and great challenge in data mining, especially for large-scale datasets. On the one hand, traditional algorithms often achieve low-quality detection results and are sensitive to neighborhood size. On the other hand, they are infeasible for large-scale datasets due to at least O(N²) time and space complexity. In light of these, we propose a new local outlier detection algorithm, which is designed based on a new stable neighborhood strategy-dynamic references nearest neighbors (DRNN). Meanwhile, we present a new detection framework by combining the proposed approach and k-mean for large-scale datasets. Experimental results demonstrate that the proposed algorithm can produce higher quality and robust detection results compared to several classic methods. Meanwhile, the new detection framework is able to significantly improve detecting efficiency without sacrificing accuracy.

局部异常值检测是数据挖掘中的一个热点和巨大挑战，特别是对于大规模数据集。一方面，传统算法检测结果质量低，对邻域大小敏感。另一方面，由于至少 O(N ²) 时间和空间复杂度。鉴于这些，我们提出了一种新的局部异常值检测算法，该算法基于一种新的稳定邻域策略——动态参考最近邻（DRNN）而设计。同时，我们通过将所提出的方法和大规模数据集的 k-mean 相结合，提出了一种新的检测框架。实验结果表明，与几种经典方法相比，所提出的算法可以产生更高质量和鲁棒性的检测结果。同时，新的检测框架能够在不牺牲准确性的情况下显着提高检测效率。