OUTLIER DETECTION USING CLUSTER-BASED APPROACH: A REVIEW

Abstract

Outlier detection is a crucial task in data mining which aims to detect an outlier from given data set. The data is said to be an outlier which appears to have inconsistent observation with the remaining data. Outliers are generated because of improper measurements, data entry errors or data arriving from various sources than remaining data. 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 such as distance threshold, density threshold, etc. The goal of this proposed work is to partition the input data set into the number of clusters and then outlier is detected for each cluster. The computational time is affected as the data set size is reduced in first phase due to clustering. This work aims at studying two-three different methods of outlier detection with two different data sets. Also analyzing the performance of each method based on system accuracy.