A SURVEY OF 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 sensor 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, density-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.