New Approach Based on Square Neighborhood to Detect Outliers
Tianqiang Huang, YE Fei-yue · Kongzhi yu juece · 2006
A new quick density-based approach to detect outliers,called outlier detecting based on square neighborhood(ODBSN),is presented.This algorithm changes the e-neighborhood in DBSCAN to a square neighborhood and judges if the neighbors in the dense square neighborhood are not outlier.The algorithm partitions objects with square neighborhood,not with spatial grids,and thus does not cause dimension curse.The algorithm can indicate the degree of outlier with the local deviate factor,so the outlier can be identified exactly and the precision is measurable.Theoretical comparison shows that this method is more efficient than the well-known algorithm based on density,DBSCAN and LOF.Experimental results more efficient that the proposed approach can effectively identify outliers in databases within clusters that have different shape and varied density,and it is several times faster than the original DBSCAN and LOF algorithm.