Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional outliers that reflect unusual responses associated with a given context or co...

Univariate outlier analysis has become a popular approach for improving quality. When a customer return occurs, multivariate outlier analysis extends the univariate analysis to develop a test model for preventing similar returns from happening. In this context, this work investigates the following question: How simple multivariate outlier modeling can be? The interest for answering this questio...

Rather than attempt an encyclopedic survey of nonparametric and robust multivariate methods, we limit to a manageable scope by focusing on just two leading and pervasive themes, descriptive statistics and outlier identification. We set the stage with some perspectives, and we conclude with a look at some open issues and directions. A variety of questions are raised. Is nonparametric inference t...

Data objects which differ significantly from the remaining data objects are referred to as outliers. Density-based algorithms for mining outliers are very effective in detecting all forms of outliers, where data objects with fewer neighbors are likely to be outliers than are those with more neighbors. However, existing density-based algorithms engage in huge repetitive computation and compariso...

Detecting anomalous objects from given data has a broad range of real-world applications. Although there is rich number outlier detection algorithms, most them involve hidden assumptions and restrictions. This paper proposes novel, yet effective learning algorithm that based on decomposing the full attributes space into different combinations subspaces, in which 3D-vectors, representing points ...