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dc.contributor.authorZorarpacı, Ezgi
dc.date.accessioned2023-08-14T06:37:34Z
dc.date.available2023-08-14T06:37:34Z
dc.date.issued2022en_US
dc.identifier.citationZorarpacı, E. (2023). Data clustering using leaders and followers optimization and differential evolution. Applied Soft Computing, 132, art. no. 109838. https://doi.org/10.1016/j.asoc.2022.109838en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2022.109838
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2599
dc.description.abstractData clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficul-ties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.asoc.2022.109838en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData clusteringen_US
dc.subjectData miningen_US
dc.subjectDifferential evolutionen_US
dc.subjectLeaders and followersen_US
dc.subjectParameter-free optimizationen_US
dc.subject.classificationData Clustering
dc.subject.classificationK-Mean Algorithm
dc.subject.classificationCluster Analysis
dc.subject.classificationElectrical Engineering, Electronics & Computer Science - Artificial Intelligence & Machine Learning - Clustering
dc.subject.otherCluster analysis
dc.subject.otherClustering algorithms
dc.subject.otherComputational efficiency
dc.subject.otherData mining
dc.subject.otherEvolutionary algorithms
dc.subject.otherClustering methods
dc.subject.otherData clustering
dc.subject.otherDifferential evolution
dc.subject.otherDifferential evolution algorithms
dc.subject.otherLeader and follower
dc.subject.otherOptimisations
dc.subject.otherParameter free optimisation
dc.subject.otherPartitional clustering
dc.subject.otherPerformance metrices
dc.subject.otherSearch spaces
dc.subject.otherOptimization
dc.titleData clustering using leaders and followers optimization and differential evolutionen_US
dc.typearticleen_US
dc.relation.journalApplied Soft Computingen_US
dc.contributor.departmentHavacılık ve Uzay Bilimleri Fakültesi -- Havacılık Yönetimien_US
dc.identifier.volume132en_US
dc.identifier.issueArticle number 109838en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorZorarpacı, Ezgi
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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