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Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric and non-parametric approaches in a centralized setting. However, there are still several challenges that must be addressed. First, most approaches to date have focused on detecting outliers in a continuous attribute space. However, almost all real-world data sets contain a mixture of categorical and continuous attributes. Categorical attributes are typically ignored or incorrectly modeled by existing approaches, resulting in a significant loss of information. Second, there have not been any general-purpose …