Optimizing Euclidean Distance Computation

This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, bei...

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Veröffentlicht in:	Mathematics (Basel) Jg. 12; H. 23; S. 3787
1. Verfasser:	Mussabayev, Rustam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2024
Schlagworte:	Algorithms Bioinformatics Clustering Comparative analysis Computational linguistics computational optimization Data analysis Data mining Data structures Datasets Efficiency Energy conservation Energy consumption Euclidean distance Euclidean geometry Euclidean space Geospatial data Germany Information management K-means clustering K-nearest neighbors (KNNs) Language processing Linear algebra Machine learning Mathematical optimization Methods Natural language interfaces Optimization parallelization Spatial data Task complexity vectorization Germany
ISSN:	2227-7390, 2227-7390
Online-Zugang:	Volltext
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