A fast two-level approximate euclidean minimum spanning tree algorithm for high-dimensional data

Abstract

Euclidean minimum spanning tree algorithms run typically with quadratic computational complexity, which is not practical for large scale high dimensional datasets. In this paper, we propose a new two-level approximate Euclidean minimum spanning tree algorithm for high dimensional data. In the first level, we perform outlier detection for a given data set to identify a small amount of boundary points and run standard Prim’s algorithm on the reduced dataset. In the second level, we conduct a k-nearest neighbors search to complete an approximate Euclidean Minimum Spanning Tree construction process. Experimental results on sample data sets demonstrate the efficiency of the proposed method while keeping high approximate precision.