Data Management

Performance Evaluation of Density-Based Outlier Detection on High Dimensional Data

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Executive Summary

Outlier detection is a task that finds objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data. Outlier detection has wide applications which include data analysis, financial fraud detection, network intrusion detection and clinical diagnosis of diseases. In data analysis applications, outliers are often considered as error or noise and are removed once detected. Approaches to detect and remove outliers have been studied by several researchers. Density based approaches have been proved to be effective in detecting outliers successfully, but usually requires huge amount of computations.

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