Approximating Expensive Distance Metrics
| dc.contributor.author | Pryor, Elliott | |
| dc.contributor.author | Stouffer, Nathan | |
| dc.date.accessioned | 2021-12-08T20:40:45Z | |
| dc.date.available | 2021-12-08T20:40:45Z | |
| dc.date.issued | 2021-11 | |
| dc.description.abstract | Computing the distance between point a and point b is typically considered to be very easy. However, there are times when computing a distance can take significant computation time; we call these expensive distance metrics. Suppose we have some expensive distance metric and we need to compute the distances between a bunch of points. This paper explores a method that to reduce the number of queries to the distance metric and the effect on clustering. The authors find that total run time can be reduced while only inducing small inaccuracies in clustering output. | en_US |
| dc.description.sponsorship | This project was done during a course project in Computational Geometry, under the guidance of Dave Millman. | |
| dc.identifier.citation | Pryor, Elliott and Nathan Stouffer. (2021). “Approximating Expensive Distance Metrics.” Curiositas 1, no 1. (15 November, 2021) 22-27. DOI: 10.15788/f2021.curio5 | en_US |
| dc.identifier.uri | https://scholarworks.montana.edu/handle/1/16570 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Montana State University | en_US |
| dc.rights | Copyright 2021 Pryor and Stouffer | en_US |
| dc.title | Approximating Expensive Distance Metrics | en_US |
| dc.type | Article | en_US |
| mus.citation.extentfirstpage | 22 | en_US |
| mus.citation.extentlastpage | 27 | en_US |
| mus.citation.issue | 1 | en_US |
| mus.citation.journaltitle | Curiositas | en_US |
| mus.citation.volume | 1 | en_US |
| mus.data.thumbpage | 6 | en_US |
| mus.identifier.doi | 10.15788/f2021.curio5 | en_US |
| mus.relation.college | College of Engineering | en_US |
| mus.relation.department | Computer Science. | en_US |
| mus.relation.university | Montana State University - Bozeman | en_US |