Explainable Distance-Based Outlier Detection in Data Streams

Explainable Distance-Based Outlier Detection in Data Streams

Blog Article

Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the blondor pale quartz identification of the relevant dimensions.We extend this rationale for unsupervised distance-based outlier detection, and through investigating subspaces, we propose a novel labeling of outliers in a manner that is intuitive for the user and does not require any training at runtime.Moreover, our solution is applicable to online settings and a complete prototype for detecting and explaining outliers in data streams using massive parallelism has been implemented.Our solution is evaluated in terms of both the quality of the labels 15-fd0038ca derived and the performance.