A Neural Probabilistic outlier detection method for categorical data

Unsupervised outlier detection for categorical data is important and essential for broad applications in various domains. The complex interactions between attributes and the relevance of attributes make it a stem challenge. Existing methods, including patterns-based and couplings-based methods, either fail to capture the complex interactions or cannot handle the diverse attributes well. In this paper, we propose a novel Neural Probabilistic...