A novel subspace outlier detection method by entropy-based clustering algorithm

Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the "curse of dimensionality". Subspace outlier detection methods have great potential to overcome the problem. However, the challenge becomes how to determine which subspaces to be used for outlier detection among a huge number of all subspaces. In this paper, firstly, we propose an intuitive definition of outliers in subspaces. We study the desirable properties of subspaces for outlier detection and investigate the metrics for those properties. Then, a novel subspace outlier detection algorithm with a statistical foundation is proposed. Our method selectively leverages a limited set of the most interesting subspaces for outlier detection. Through experimental validation, we demonstrate that identifying outliers within this reduced set of highly interesting subspaces yields significantly higher accuracy compared to analyzing the entire feature space. We show by experiments that the proposed method outperforms competing subspace outlier detection approaches on real world data sets.