Automatic centroid initialization in k-means using artificial hummingbird algorithm.
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| Title: | Automatic centroid initialization in k-means using artificial hummingbird algorithm. |
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| Authors: | Preeti1,2 (AUTHOR) preeti@ma.iitr.ac.in, Deep, Kusum1 (AUTHOR) kusum.deep@ma.iitr.ac.in |
| Source: | Neural Computing & Applications. Feb2025, Vol. 37 Issue 5, p3373-3398. 26p. |
| Subjects: | Computer workstation clusters, Artificial intelligence, Image processing, Cluster analysis (Statistics), Heuristic, Centroid |
| Abstract: | K-means is a widely used technique that heavily relies on the initial cluster centroid location. Poorly chosen centroids can cause the algorithm to get trapped in suboptimal solutions. Additionally, determining the optimal number of clusters for large datasets is computationally expensive. To address these challenges, a recently developed Artificial Hummingbird Algorithm (AHA) is used to initialize cluster centroid locations and automatically determine the best estimate for the number of clusters. AHA simulates the specialized flight skills and intelligent foraging strategies of hummingbirds, striking a fine balance between exploration and exploitation during the search process. Unlike other data clustering approaches that use a fixed threshold in heuristic methods, we propose a dynamic threshold based on the variance of the data with respect to its centroids for activating cluster centroids in AHA. The data are automatically partitioned into k cluster centroids such that cohesion, measured by cluster diameters, and separation, measured by nearest neighbor distance, are optimized. The algorithm is tested on various datasets, including real-world data, fundamental clustering benchmarks, synthetic data, and high-dimensional data. To evaluate performance, metrics such as fitness value, inter-cluster distance, and intra-cluster distance were used. Results indicate that the proposed method ranked first and achieved superior clustering performance compared to state-of-the-art algorithms. [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: 182883042 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic centroid initialization in k-means using artificial hummingbird algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Preeti%22">Preeti</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> preeti@ma.iitr.ac.in</i><br /><searchLink fieldCode="AR" term="%22Deep%2C+Kusum%22">Deep, Kusum</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kusum.deep@ma.iitr.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Feb2025, Vol. 37 Issue 5, p3373-3398. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+workstation+clusters%22">Computer workstation clusters</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Centroid%22">Centroid</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: K-means is a widely used technique that heavily relies on the initial cluster centroid location. Poorly chosen centroids can cause the algorithm to get trapped in suboptimal solutions. Additionally, determining the optimal number of clusters for large datasets is computationally expensive. To address these challenges, a recently developed Artificial Hummingbird Algorithm (AHA) is used to initialize cluster centroid locations and automatically determine the best estimate for the number of clusters. AHA simulates the specialized flight skills and intelligent foraging strategies of hummingbirds, striking a fine balance between exploration and exploitation during the search process. Unlike other data clustering approaches that use a fixed threshold in heuristic methods, we propose a dynamic threshold based on the variance of the data with respect to its centroids for activating cluster centroids in AHA. The data are automatically partitioned into k cluster centroids such that cohesion, measured by cluster diameters, and separation, measured by nearest neighbor distance, are optimized. The algorithm is tested on various datasets, including real-world data, fundamental clustering benchmarks, synthetic data, and high-dimensional data. To evaluate performance, metrics such as fitness value, inter-cluster distance, and intra-cluster distance were used. Results indicate that the proposed method ranked first and achieved superior clustering performance compared to state-of-the-art algorithms. [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/s00521-024-10764-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 3373 Subjects: – SubjectFull: Computer workstation clusters Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Image processing Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Heuristic Type: general – SubjectFull: Centroid Type: general Titles: – TitleFull: Automatic centroid initialization in k-means using artificial hummingbird algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Preeti – PersonEntity: Name: NameFull: Deep, Kusum IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 5 Titles: – TitleFull: Neural Computing & Applications Type: main |
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