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Article

Similarity between Euclidean and cosine angle distance for nearest neighbor queries

Authors:

Sakti PramanikAuthors Info & Claims

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

Pages 1232 - 1237

https://doi.org/10.1145/967900.968151

Published: 14 March 2004 Publication History

Abstract

Understanding the relationship among different distance measures is helpful in choosing a proper one for a particular application. In this paper, we compare two commonly used distance measures in vector models, namely, Euclidean distance (EUD) and cosine angle distance (CAD), for nearest neighbor (NN) queries in high dimensional data spaces. Using theoretical analysis and experimental results, we show that the retrieval results based on EUD are similar to those based on CAD when dimension is high. We have applied CAD for content based image retrieval (CBIR). Retrieval results show that CAD works no worse than EUD, which is a commonly used distance measure for CBIR, while providing other advantages, such as naturally normalized distance.

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Published In

cover image ACM Conferences

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

March 2004

1733 pages

ISBN:1581138121

DOI:10.1145/967900

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Program Chairs:
Andrea Omicini
Università degli Studi di Bologna a Cesena
,
Roger L. Wainwright
University of Tulsa
,
Publications Chair:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology

Copyright © 2004 ACM.

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Published: 14 March 2004

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https://doi.org/10.1109/MeMeA60663.2024.10596816
Kritsrinopphadol TSuvirat KChairat STangudomkit KIngviya TBovornchutichai PChaichulee S(2024)Measuring Patient Similarities in Clinical Data Repository through Graph Representation2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613727(500-507)Online publication date: 19-Jun-2024
https://doi.org/10.1109/JCSSE61278.2024.10613727
Weber DGühmann CSeel T(2024)FranSys—A Fast Non-Autoregressive Recurrent Neural Network for Multi-Step Ahead PredictionIEEE Access10.1109/ACCESS.2024.347301412(145130-145147)Online publication date: 2024
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