A New Computational Approach Based on Density Clustering for Outlier Problems in Linear Models

Abstract

Recently, collection of huge amount of data and analysis of that much data have vital importance for human activities in many different application areas. Advanced statistical methods play crucial role for modeling of such data when the data contains outliers. Although there are number of outlier detection methods for revealing outlier observations in data, most of them may not be reasonable and appropriate for prediction purposes due to structural and requirements of modeling. In this study, density based clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is considered in order to detect the location of outlier observations effectively with respect to form of the model for given data set. Based on obtained results, the Mean Shift Outlier Model (MSOM) is constructed as a robust linear model. This newly proposed computational approach based on DBSCAN uses power of data clustering and also minimize the impact of the outlier observations by MSOM. The numerical examples are also presented to reveal the performance of the proposed approach in this study.

Keywords

• Outlier problem
• Mean shift outlier model
• Density based clustering

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