Zhang, Ke; Jin, Huidong (Warren)
Detecting outliers in mixed attribute datasets is one of major challenges in real world applications. Existing outlier detection methods lack effectiveness for mixed attribute datasets mainly due to their inability of considering interactions among different types of, e.g., numerical and categorical attributes. To address this issue in mixed attribute datasets, we propose a novel Pattern based Outlier Detection approach (POD). Pattern in this paper is defined to describe majority of data as...[Show more]
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