A New Fine-Kinney Method Based on Clustering Approach

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dc.contributor.authorDagsuyu, Cansu
dc.contributor.authorOturakci, Murat
dc.contributor.authorEssiz, Esra Sarac
dc.date.accessioned2025-01-06T17:37:53Z
dc.date.available2025-01-06T17:37:53Z
dc.date.issued2020
dc.description.abstractIn this study, a new approach to Fine-Kinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with K-Means and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, K-Means algorithms have provided accurate and sensitive cluster of classes. Classes from conventional and K-Means algorithms are applied and compared to the identified risks of a workshop of a medium sized textile company. Results of the study indicate that clustering techniques are new, original and applicable way to define new classes in order to prioritize risks by overcoming the drawbacks of conventional Fine-Kinney method.
dc.identifier.doi10.1142/S0218488520500208
dc.identifier.endpage512
dc.identifier.issn0218-4885
dc.identifier.issn1793-6411
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85085368497
dc.identifier.scopusqualityQ3
dc.identifier.startpage497
dc.identifier.urihttps://doi.org/10.1142/S0218488520500208
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2404
dc.identifier.volume28
dc.identifier.wosWOS:000537358800006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.relation.ispartofInternational Journal of Uncertainty Fuzziness and Knowledge-Based Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectFine Kinney method
dc.subjectclustering algorithms
dc.subjectK-means algorithms
dc.titleA New Fine-Kinney Method Based on Clustering Approach
dc.typeArticle

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