Outlier Detection using Cluster-Based Approach

Data mining has the crucial task of Outlier detection 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 using Unsupervised Extreme Learning Machine algorithm. Then the clusters are given as an input to the outlier detection methods namely cluster based outlier detection algorithm and outlier detection algorithm. The methods detects an outlier from each cluster. This work aims at studying cluster based outlier detection algorithm and outlier detection algorithm with different data sets. Also analyzing the performance of each method based on outlier detection accuracy.