Outlier Based Fraud Detection System

Abstract

Data mining has the vital task of Outlier detection which aims to detect an outlier from given datasets. The analysis or detection of outlier data is referred to as Outlier Mining. In Data mining, outlier detection is the identification of unusual or distant data records that might be require further investigation or analysis. This paper provides the data driven methods for various fraud detection systems based on literature review, fraudulent activities or cases and comparative research. Outlier detection is the technique which discovers such type of data from the given data set. Several techniques of outlier detection have been introduced which requires input parameter from the user. The goal of this proposed work is to partition the input data set into the number of clusters using K-NN algorithm. Then the clusters are given as an input to the outlier detection methods namely cluster based outlier algorithm and Local Outlier Factor Algorithm. The Performance evaluation of this algorithm confirms that our approach of finding local outliers can be practically implemented.