Noise Clustering based on Local Outlier Factor

Abstract

Noise clustering is a clustering method that classifies outliers as noise clusters and is known as a method to reducing the influence of outliers. Local Outlier Factor is an anomaly detection method that quantifies the degree of data outliers based on the density with neighboring data. In this study, we propose a new noise clustering method that introduces the Local Outlier Factor. Numerical experiments show that the proposed method is more robust to outliers than existing methods for artificial and real datasets.