VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation

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dc.authoridSENOL, Ali/0000-0003-0364-2837
dc.contributor.authorSenol, Ali
dc.date.accessioned2025-03-17T12:25:34Z
dc.date.available2025-03-17T12:25:34Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractThe cluster evaluation process is of great importance in areas of machine learning and data mining. Evaluating the clustering quality of clusters shows how much any proposed approach or algorithm is competent. Nevertheless, evaluating the quality of any cluster is still an issue. Although many cluster validity indices have been proposed, there is a need for new approaches that can measure the clustering quality more accurately because most of the existing approaches measure the cluster quality correctly when the shape of the cluster is spherical. However, very few clusters in the real world are spherical. Therefore, a new Validity Index for Arbitrary-Shaped Clusters based on the kernel density estimation (the VIASCKDE Index) to overcome the mentioned issue was proposed in the study. In the VIASCKDE Index, we used separation and compactness of each data to support arbitrary-shaped clusters and utilized the kernel density estimation (KDE) to give more weight to the denser areas in the clusters to support cluster compactness. To evaluate the performance of our approach, we compared it to the state-of-the-art cluster validity indices. Experimental results have demonstrated that the VIASCKDE Index outperforms the compared indices.
dc.identifier.doi10.1155/2022/4059302
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.pmid35720897
dc.identifier.scopus2-s2.0-85132312847
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1155/2022/4059302
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1750
dc.identifier.volume2022
dc.identifier.wosWOS:000815095300019
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSenol, Ali
dc.language.isoen
dc.publisherHindawi Ltd
dc.relation.ispartofComputational Intelligence and Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectValidation
dc.titleVIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation
dc.typeArticle

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