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Outlier Detection is a critical and cardinal research task due its array ofapplications in variety of domains ranging from data mining, clustering,statistical analysis, fraud detection, network intrusion detection anddiagnosis of diseases etc. Over the last few decades, distance-based outlierdetection algorithms have gained significant reputation as a viable alternativeto the more traditional statistical approaches due to their scalable,non-parametric and simple implementation. In this paper, we present a modifiedonion peeling (Convex hull) genetic algorithm to detect outliers in a Gaussian2-D point data set. We present three different scenarios of outlier detectionusing a) Euclidean Distance Metric b) Standardized Euclidean Distance Metricand c) Mahalanobis Distance Metric. Finally, we analyze the performance andevaluate the results.

Onion-Peeling Outlier Detection in 2-D data Sets