An improved location difference of multiple distances based nearest neighbors searching algorithm

Abstract

The location difference of multiple distances based nearest neighbors search algorithm (LDMDBA) has a good performance in efficiency compared with other kNN algorithm. The major advantage of it is its precision is litter lower than the full search algorithm (FSA) algorithm. In this paper, we proposed an improved LDMDBA algorithm (ILDMDBA) by increasing the number of the reference points from log(d) to d, where the d is the dimensionality of data set. By this way, the prediction of ILDMDBA is improved. Our analysis results show that the time complexity of the proposed algorithm is not increased. The effectiveness and efficiency of the proposed algorithm are demonstrated in experiments involving public and artificial datasets.