MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters.
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| Title: | MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters. |
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| Authors: | Şenol, Ali1 (AUTHOR) alisenol@tarsus.edu.tr |
| Source: | Neural Computing & Applications. Jun2023, Vol. 35 Issue 18, p13239-13259. 21p. |
| Subjects: | Spanning trees, Microclusters, Data structures, Statistics, Data analysis |
| Abstract: | Clustering 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 163798672 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Şenol%2C+Ali%22">Şenol, Ali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alisenol@tarsus.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2023, Vol. 35 Issue 18, p13239-13259. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Spanning+trees%22">Spanning trees</searchLink><br /><searchLink fieldCode="DE" term="%22Microclusters%22">Microclusters</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Clustering 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-023-08386-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 13239 Subjects: – SubjectFull: Spanning trees Type: general – SubjectFull: Microclusters Type: general – SubjectFull: Data structures Type: general – SubjectFull: Statistics Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Şenol, Ali IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 35 – Type: issue Value: 18 Titles: – TitleFull: Neural Computing & Applications Type: main |
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