In this paper, we present a new definition for outliers: cluster-based outlier, which is meaningful and provides importance to the local data behavior, and how to detect outliers by the clustering algorithm LDBSCAN which is capable of finding clusters and assigning LOF to single points.
提出一个新的概念——基于簇的孤立点概念,这个概念的提出有助于理解局部数据的行为,同时也描述了如何利用LDBSCAN算法发现基于簇的孤立点,并为每一个对象计算局部偏离因子。