A deep embedded clustering technique using dip test and unique neighbourhood set.
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| Title: | A deep embedded clustering technique using dip test and unique neighbourhood set. |
|---|---|
| Authors: | Rahman, Md Anisur1 (AUTHOR) anisur.rahman@latrobe.edu.au, Ang, Li-minn2 (AUTHOR) lang@usc.edu.au, Sun, Yuan1 (AUTHOR) yuan.sun@latrobe.edu.au, Seng, Kah Phooi3 (AUTHOR) jasmine.seng@xjtlu.edu.cn |
| Source: | Neural Computing & Applications. Jan2025, Vol. 37 Issue 3, p1345-1356. 12p. |
| Subjects: | Selection (Plant breeding), Deterministic processes, Neighborhoods, Seeds, Algorithms, Deep learning |
| Abstract: | In recent years, there has been a growing interest in deep learning-based clustering. A recently introduced technique called DipDECK has shown effective performance on large and high-dimensional datasets. DipDECK utilises Hartigan's dip test, a statistical test, to merge small non-viable clusters. Notably, DipDECK was the first deep learning-based clustering technique to incorporate the dip test. However, the number of initial clusters of DipDECK is overestimated and the algorithm then randomly selects the initial seeds to produce the final clusters for a dataset. Therefore, in this paper, we presented a technique called UNSDipDECK , which is an improved version of DipDECK and does not require user input for datasets with an unknown number of clusters. UNSDipDECK produces high-quality initial seeds and the initial number of clusters through a deterministic process. UNSDipDECK uses the unique closest neighbourhood and unique neighbourhood set approaches to determine high-quality initial seeds for a dataset. In our study, we compared the performance of UNSDipDECK with fifteen baseline clustering techniques, including DipDECK, using NMI and ARI metrics. The experimental results indicate that UNSDipDECK outperforms the baseline techniques, including DipDECK. Additionally, we demonstrated that the initial seed selection process significantly contributes to UNSDipDECK 's ability to produce high-quality clusters. [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: 182466845 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A deep embedded clustering technique using dip test and unique neighbourhood set. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rahman%2C+Md+Anisur%22">Rahman, Md Anisur</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anisur.rahman@latrobe.edu.au</i><br /><searchLink fieldCode="AR" term="%22Ang%2C+Li-minn%22">Ang, Li-minn</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lang@usc.edu.au</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yuan%22">Sun, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yuan.sun@latrobe.edu.au</i><br /><searchLink fieldCode="AR" term="%22Seng%2C+Kah+Phooi%22">Seng, Kah Phooi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jasmine.seng@xjtlu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jan2025, Vol. 37 Issue 3, p1345-1356. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Selection+%28Plant+breeding%29%22">Selection (Plant breeding)</searchLink><br /><searchLink fieldCode="DE" term="%22Deterministic+processes%22">Deterministic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Neighborhoods%22">Neighborhoods</searchLink><br /><searchLink fieldCode="DE" term="%22Seeds%22">Seeds</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, there has been a growing interest in deep learning-based clustering. A recently introduced technique called DipDECK has shown effective performance on large and high-dimensional datasets. DipDECK utilises Hartigan's dip test, a statistical test, to merge small non-viable clusters. Notably, DipDECK was the first deep learning-based clustering technique to incorporate the dip test. However, the number of initial clusters of DipDECK is overestimated and the algorithm then randomly selects the initial seeds to produce the final clusters for a dataset. Therefore, in this paper, we presented a technique called UNSDipDECK , which is an improved version of DipDECK and does not require user input for datasets with an unknown number of clusters. UNSDipDECK produces high-quality initial seeds and the initial number of clusters through a deterministic process. UNSDipDECK uses the unique closest neighbourhood and unique neighbourhood set approaches to determine high-quality initial seeds for a dataset. In our study, we compared the performance of UNSDipDECK with fifteen baseline clustering techniques, including DipDECK, using NMI and ARI metrics. The experimental results indicate that UNSDipDECK outperforms the baseline techniques, including DipDECK. Additionally, we demonstrated that the initial seed selection process significantly contributes to UNSDipDECK 's ability to produce high-quality clusters. [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/s00521-024-10497-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1345 Subjects: – SubjectFull: Selection (Plant breeding) Type: general – SubjectFull: Deterministic processes Type: general – SubjectFull: Neighborhoods Type: general – SubjectFull: Seeds Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A deep embedded clustering technique using dip test and unique neighbourhood set. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rahman, Md Anisur – PersonEntity: Name: NameFull: Ang, Li-minn – PersonEntity: Name: NameFull: Sun, Yuan – PersonEntity: Name: NameFull: Seng, Kah Phooi IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Neural Computing & Applications Type: main |
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