Pattern based Outlier Detection in Mixed-Attribute Datasets
نویسندگان
چکیده
Outlier detection in mixed attribute datasets has proved to be a challenging task required in real world applications. Most existing algorithms for outlier detection do not consider the interactions between categorical and numerical attributes. The Pattern based Outlier Detection (POD) algorithm (Zhang & Jin, 2011), has had considerable success in the detecting outliers by analysing such interactions. In this report, the Pattern based Outlier Detection using Support Vector Machines (POD-SVM) algorithm for outlier detection is presented that tries to improve upon the results of POD. POD-SVM makes use of Support Vector Machines to detect patterns in datasets and thus, calculate an outlier score for records in a mixed attribute dataset. Experiments carried out illustrate that the POD-SVM algorithm provides results that are at least as good as the results obtained by the POD algorithm.
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An Effective Pattern Based Outlier Detection Approach for Mixed Attribute Data
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