Outlier Detection Based on the Data Structure

Outlier detection is one of the most frequently demanded task for optimizing results. Distance-based methods are a popular approach. They require no prior assumptions about the data generating distribution and are uncomplicated to implement. However, related methods have different parameters that are difficult to determine such that the identification results are generally unstable. Presenting related techniques without sacrificing stability is a challenging task. In this paper, we propose a new distance-based method that depends on the data structure to detect such points. In the proposed method, a global binary tree is constructed and the local distance score of a point is calculated to evaluate to what degree the observation is an outlier. The greater the value of the distance score, the more likely the point is an outlier point. Unlike typical distance-based methods, our algorithm has good scalability. Even when the dimension of the data points increases, the performance of our algorithm does not diminish. To reduce extra parameters, the top-p ranked points can be identified as outliers. Experimental results on synthetic and real-world datasets demonstrate the effectiveness and stability of our method.