A novel hybridization approach to improve the critical distance clustering algorithm: Balancing speed and quality

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dc.contributor.authorKuwil, Farag Hamed
dc.contributor.authorAtila, Uemit
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractClustering is a prominent research area, with numerous studies and the development of hundreds of algorithms over the years. However, a fundamental challenge in clustering research is the trade-off between algorithm speed and clustering quality. Existing algorithms tend to prioritize either fast execution with compromised clustering quality or slower performance with superior clustering results. In this study, we propose a novel CDC-2 algorithm, an improved version of the Critical Distance Clustering (CDC) algorithm, to address this challenge. Inspired by the concepts of hybridization in biology and the division of labor in the economic system, we present a new hybridization strategy. Our approach integrates the connectivity and coherence aspects of the K-means and CDC-2 algorithms, respectively, allowing us to combine speed and quality in a single algorithm. This approach is referred to as the CDC++ algorithm, and it is characterized as a hybrid that combines elements from two algorithms, K-means and CDC-2, in order to leverage their strengths while mitigating their weaknesses. Moreover, the structure and mechanism of the CDC++ algorithm led to the introduction of a new concept called object autoencoder. Unlike traditional feature reduction methods, this concept focuses on object reduction, representing a significant advancement in clustering techniques. To validate our approach, we conducted experimental studies on thirteen synthetic and five real datasets. Comparative analysis with four well-known algorithms demonstrates that our proposed development and hybridization enable efficient processing of largescale and high-dimensional datasets without compromising clustering quality.en_US
dc.identifier.doi10.1016/j.eswa.2024.123298
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85183926738en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.123298
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4633
dc.identifier.volume247en_US
dc.identifier.wosWOS:001173984000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlgorithm hybridizationen_US
dc.subjectAlgorithm specializationen_US
dc.subjectClustering analysisen_US
dc.subjectCritical distanceen_US
dc.subjectConnectivity and coherenceen_US
dc.titleA novel hybridization approach to improve the critical distance clustering algorithm: Balancing speed and qualityen_US
dc.typeArticleen_US

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