For outlier detection difficulty of data sets with complex distribution and various types of outliers

a new outlier detection method based on reversed

k

-nearest neighborhood MST of relative distance measure is proposed. Firstly

relative distance of object is defined with the combination of classical distance

local density and neighborhood of object

which can be used to detect global outliers and local outliers both. Secondly

on basis of minimum spanning tree structure

by tactics of maximum-edge-cutting

outliers and outlier clusters can be obtained. Finally

experiments of synthetic and UCI data sets show that the new algorithm is much more correct and effective. It is a new effective way for detecting outliers of dat

a sets with abnormal distribution and diversity outlier types.