Survey on Outlier Detection in Data Mining

Abstract

—Outlier is a data point that deviates too much from the rest of dataset. Most of real-world dataset have outlier. Outlier detection plays an important role in data mining field. Outlier Detection is useful in many fields like Network intrusion detection, Credit card fraud detection, stoke market analysis, detecting outlying in wireless sen sor network data, fault diagnosis in machines, etc. This paper is a survey on different Outlier detection approaches, which are statistical-based approach, deviation-based approach, distance-based approach, den sity-based approach. In order to deal with outlier, clustering method is used. For that K-mean is widely used to cluster the dataset then we can apply any technique for finding outliers.