Fraud Detection Using Outlier Analysis: A Survey
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 2, Issue 6
Abstract
Outlier detection is a primary step in many data-mining applications. There are several methods for outlier detection, like univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining isoften based on distance measures, clustering and spatial methods. An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several causes. The measurement can be incorrectly observed, recorded or entered into the process computer, the observed datum can come from a different population with respect to the normal situation and thus is correctly measured but represents a rare event. An outlier is an observation that deviates so much from other observations as to arouse suspicions that is was generated by a different mechanism.
Authors and Affiliations
Vidya Mohanty
Keywords
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