New K-nearest neighbor searching algorithm based on angular similarity

The k-nearest searching algorithm (KNNS) is widely used in the high dimension space. However, current KNNS use Euclidean distance to index dataset and retrieve the search object, which is not suitable for those applications based on angular similarity. In this paper, the angular similarity based on KNNS (AS-KNNS) is proposed. AS-KNNS firstly proposes the index structure (AS-INDEX) based on angular similarity, which refers to the axis and a reference-line to organize the dataset into some shell-hyper-cone, and it linearly stores them. Then it determines the storage location for the search object, making a hyper-cone which takes the line connecting the origin point and the search object as the axis, and searches the hyper-cone for k-nearest neighbors of the search object. The experiment shows that the performance of AS-KNNS is superior to those other KNNS.