Outlier Detection using Anti-hubs

The distance base outliers detection method fails to increase the dimensionality of the data. This problem occurs due to irrelevant and redundant feature because the distance between two points is less. Reverse nearest neighbors of point P is the points for which P is in their k nearest neighbor list. Antihubs are some points are frequently comes in k-nearest neighbor list of another points and some points are infrequently comes in k nearest neighbor list of different points. Latest proposes are antihub base unsupervised outlier detection method, but these propose are suffering from high computational cost of finding outlier. This is depends on data who having better dimensionality, high computation cost, time requirement to find high antihubs. To avoid this there is need to remove out irrelevant and redundant feature of high dimensionality data. It increases the efficiency by removing the redundant feature. Using feature selection method redundant feature are removed.