Discovering Local Outlier Based on Rough Clustering

Abstract

The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but also efficient.