Density-Based Approach for Outlier Detection and Removal

Abstract

This paper represents an algorithm for performing clustering and outlier detection simultaneously. As research says, clustering and outlier (anomaly) detection are not separate problems but they are co-related. So our algorithm provides a generalized solution for outlier detection as per application. It takes some threshold values as input, applies K-means algorithm for initial clustering and based on threshold values, outliers are detected. This approach is not strict to number of clusters k, but applies re-clustering where required. It helps to find local as well as global outliers of dataset. The results can be customized by varying the values of threshold limits. The algorithm works in two phases, first phase provides initial clustering using K-Means and second phase helps to find outliers.