Performance Evaluation: Ball-Tree and KD-Tree in the Context of MST

TL;DR

This paper evaluates the performance of Ball-Tree and KD-Tree data structures within a dual-tree algorithm framework for efficiently solving the Euclidean Minimum Spanning Tree problem across various spatial datasets.

Contribution

It introduces an experimental comparison of KD-Tree and Ball-Tree structures in dual-tree algorithms for EMST, highlighting their efficiency in different spatial data contexts.

Findings

Ball-Tree outperforms KD-Tree in high-dimensional datasets.

Dual-tree algorithms significantly reduce computation time for EMST.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Now a days many algorithms are invented or being inventing to find the solution for Euclidean Minimum Spanning Tree, EMST, problem, as its applicability is increasing in much wide range of fields containing spatial or spatio temporal data viz. astronomy which consists of millions of spatial data. To solve this problem, we are presenting a technique by adopting the dual tree algorithm for finding efficient EMST and experimented on a variety of real time and synthetic datasets. This paper presents the observed experimental observations and the efficiency of the dual tree framework, in the context of kdtree and ball tree on spatial datasets of different dimensions.