MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters

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dc.authoridSENOL, Ali/0000-0003-0364-2837
dc.contributor.authorSenol, Ali
dc.date.accessioned2025-03-17T12:27:38Z
dc.date.available2025-03-17T12:27:38Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractClustering is a technique for statistical data analysis and is widely used in many areas where class labels are not available. Major problems related to clustering algorithms are handling high-dimensional, imbalanced, and/or varying-density datasets, detecting outliers, and defining arbitrary-shaped clusters. In this study, we proposed a novel clustering algorithm named as MCMSTClustering (Defining Non-Spherical Clusters by using Minimum Spanning Tree over KD-Tree-based Micro-Clusters) to overcome mentioned issues simultaneously. Our algorithm consists of three parts. The first part is defining micro-clusters using the KD-Tree data structure with range search. The second part is constructing macro-clusters by using minimum spanning tree (MST) on defined micro-clusters, and the final part is regulating defined clusters to increase the accuracy of the algorithm. To state the efficiency of our algorithm, we performed some experimental studies on some state-of-the-art algorithms. The findings were presented in detail with tables and graphs. The success of the proposed algorithm using various performance evaluation criteria was confirmed. According to the experimental studies, MCMSTClustering outperformed competitor algorithms in aspects of clustering quality in acceptable run-time. Besides, the obtained results showed that the novel algorithm can be applied effectively in solving many different clustering problems in the literature.
dc.identifier.doi10.1007/s00521-023-08386-3
dc.identifier.endpage13259
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85149776876
dc.identifier.scopusqualityQ1
dc.identifier.startpage13239
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08386-3
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2363
dc.identifier.volume35
dc.identifier.wosWOS:000947412000003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSenol, Ali
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectClustering
dc.subjectKd-Tree
dc.subjectMinimum spanning tree
dc.subjectMicro-cluster
dc.subjectArbitrary-shaped clusters
dc.titleMCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters
dc.typeArticle

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