Date of Award

Spring 2023

Document Type

Thesis (Undergraduate)

Department

Computer Science

First Advisor

Deeparnab Chakrabarty

Abstract

The minimum spanning tree is a problem with important applications but for which there are no known efficient algorithms for large data sets. Locality-sensitive hashing has been used to solve the near-neighbor problem and further applications in clustering, which indicates its potential for approximating the minimum spanning tree as well. An algorithm by Sariel Har-Peled, Piotr Indyk, and Rajeev Motwani utilizes locality-sensitive hashing to provide a c-approximation of the minimum spanning tree in O(dn1+1/c log2 n) time. In this thesis, we implement and test this algorithm. We determine that the algorithm is suited to provide a better-than-random approximation of the MST with significant improvements to computational performance over classic minimum spanning tree algorithms on clustered data.

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