Evaluating the Impact of Distance Metrics on K-means Clustering Performance with Geospatial Data

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dc.contributor.authorAy, Merhad
dc.contributor.authorOzbakir, Lale
dc.date.accessioned2026-07-01T11:36:26Z
dc.date.available2026-07-01T11:36:26Z
dc.date.issued2026
dc.departmentDüzce Üniversitesi
dc.description3rd International Conference on Data, Electronics and Computing, ICDEC 2024 -- 18 September 2024 through 20 September 2024 -- Kayseri -- 344109
dc.description.abstractClustering algorithms are fundamental in data mining, machine learning, and statistical analysis, with K-means being one of the most popular due to its simplicity and efficiency. This study examines the impact of using different distance metrics (Euclidean and Vincenty) on the performance of the K-means clustering algorithm when applied to geographical data. While Euclidean distance is widely used for its straightforward computation, it may not be ideal for clustering geographical locations due to the Earth’s curvature. Vincenty’s formula, which accounts for the Earth’s ellipsoid shape, offers a potentially more accurate alternative. We conducted experiments using a dataset of 69,902 geographical points, evaluating the clustering performance based on Sum of Squared Errors (SSE), Davies-Bouldin Index (DBI), and Silhouette Index. Our results indicate that although Vincenty’s formula theoretically provides more precise distance measurements depending on the earth shape, the practical differences in clustering performance between Euclidean and Vincenty distances are minimal. Statistical analysis using the Mann–Whitney U test demonstrated that the observed differences lacked statistical significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
dc.identifier.doi10.1007/978-981-96-7742-9_24
dc.identifier.endpage351
dc.identifier.isbn978-981967741-2
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-105023499336
dc.identifier.scopusqualityQ4
dc.identifier.startpage343
dc.identifier.urihttps://doi.org/10.1007/978-981-96-7742-9_24
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23020
dc.identifier.volume1455 LNNS
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260623
dc.subjectClustering
dc.subjectEuclidean
dc.subjectK-means
dc.subjectMachine learning
dc.subjectVincenty
dc.titleEvaluating the Impact of Distance Metrics on K-means Clustering Performance with Geospatial Data
dc.typeConference Object

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