A practical outlier detection approach for mixed-attribute data
A principled approach to identify outliers in mixed-attribute data is proposed. It is based on novel outlier scoring functions and utilizes the beta mixture model. It is able to automatically discriminate outliers from inliers. It can be applied to both mixed-type attribute and single-type attribute data. It achieves competitive results in comparison to mainstream approaches.
outlier detection mixed-attribute data mixture model data mining bivariate beta