{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-geometrical-extraction-of-nearest","title":"Fast geometrical extraction of nearest neighbors from multi-dimensional data","arxiv_id":null,"date":"2023-04-01","proceeding":"Pattern Recognition Journal 2023 4","authors":["Yasir Aziz","Kashif Hussain Memon"],"abstract":"K-Nearest Neighbor (KNN) algorithm plays a significant role in various fields of data science and machine learning. Most variants of the KNN algorithm involve distance computations and a parameter (K) that represents the required number of neighbors. The recent research regarding distance computations and finding the optimal value of K have made neighborhood extraction a slow process. This research presents a fast geometrical approach for neighborhood extraction from multi-dimensional data. Instead of distance computations, the proposed algorithm creates a geometrical shape based on the number of features of data. This geometrical shape encompasses the reference data point and the neighboring points. The pro- posed algorithm’s efficiency of time, classification, and hashing are evaluated and compared with existing state-of-the-art algorithms.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0031320322006628","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S0031320322006628","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-geometrical-extraction-of-nearest","repo_url":"https://github.com/FA19C2PC001/FGENN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}