Algorithm of KNNS based on angular similarity

The KNNS is widely used in the high dimension space.However,the current KNNS uses Euclidean distance to index dataset and retrieve the target object,which is not suitable for those applications based on angular similarity.This paper proposed the angular similarity based on KNNS(BA-KNNS).BA-KNNS firstly proposed that the indexing structure should be based on angular similarity,refered to a center line and a referenced line to organize dataset with the method of the shell-hypercone,and stored them linearly.Then it determined the space place for the target object,making a hypercone which took the line connecting the origin point and the target object as center,and searched the hypercone for the target.The experiment shows that the performance of BA-KNNS is superior to those other KNNS.