A fast MST-inspired kNN-based outlier detection method
作者：
Highlights：
• A new k-nearest neighbors (kNN) based outlier detection scheme is proposed.
• It is built upon two new MST-inspired outlier scores, a global one and a local one.
• A set of state-of-the-art outlier detectors are applied to some high dimensional data.
• A fast approximate kNN search algorithm is used to accelerate the mining process.
• The proposed method can provide competing performances with existing solutions.
摘要
•A new k-nearest neighbors (kNN) based outlier detection scheme is proposed.•It is built upon two new MST-inspired outlier scores, a global one and a local one.•A set of state-of-the-art outlier detectors are applied to some high dimensional data.•A fast approximate kNN search algorithm is used to accelerate the mining process.•The proposed method can provide competing performances with existing solutions.
论文关键词：Distance-based outlier detection,Density-based outlier detection,Clustering-based outlier detection,Minimum spanning tree-based clustering,Approximate k-nearest neighbors’ search
论文评审过程：Received 11 June 2013, Revised 25 August 2014, Accepted 9 September 2014, Available online 26 September 2014.
论文官网地址：https://doi.org/10.1016/j.is.2014.09.002
