ImpKmeans: An Improved Version of the K-Means Algorithm, by Determining Optimum Initial Centroids, based on Multivariate Kernel Density Estimation and Kd-Tree

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dc.contributor.authorŞenol, Ali
dc.date.accessioned2025-03-17T12:22:53Z
dc.date.available2025-03-17T12:22:53Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractK-means is the best known clustering algorithm, because of its usage simplicity, fast speed and efficiency. However, resultant clusters are influenced by the randomly selected initial centroids. Therefore, many techniques have been implemented to solve the mentioned issue. In this paper, a new version of the k-means clustering algorithm named as ImpKmeans shortly (An Improved Version of K-Means Algorithm by Determining Optimum Initial Centroids Based on Multivariate Kernel Density Estimation and Kd-tree) that uses kernel density estimation, to find the optimum initial centroids, is proposed. Kernel density estimation is used, because it is a nonparametric distribution estimation method, that can identify density regions. To understand the efficiency of the ImpKmeans, we compared it with some state-of-the-art algorithms. According to the experimental studies, the proposed algorithm was better than the compared versions of k-means. While ImpKmeans was the most successful algorithm in 46 tests of 60, the second-best algorithm, was the best on 34 tests. Moreover, experimental results indicated that the ImpKmeans is fast, compared to the selected k-means versions. © 2024, Budapest Tech Polytechnical Institution. All rights reserved.
dc.identifier.doi10.12700/APH.21.2.2024.2.6
dc.identifier.endpage131
dc.identifier.issn1785-8860
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85178333321
dc.identifier.scopusqualityQ1
dc.identifier.startpage111
dc.identifier.urihttps://doi.org/10.12700/APH.21.2.2024.2.6
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1433
dc.identifier.volume21
dc.indekslendigikaynakScopus
dc.institutionauthorŞenol, Ali
dc.language.isoen
dc.publisherBudapest Tech Polytechnical Institution
dc.relation.ispartofActa Polytechnica Hungarica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250316
dc.subjectcentroid initialization
dc.subjectclustering
dc.subjectk-means
dc.subjectkd-tree
dc.subjectkernel density estimation
dc.titleImpKmeans: An Improved Version of the K-Means Algorithm, by Determining Optimum Initial Centroids, based on Multivariate Kernel Density Estimation and Kd-Tree
dc.typeArticle

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