Academic Journal
Clustering Performance of a Recombinator Hartigan–Wong Algorithm.
| Title: | Clustering Performance of a Recombinator Hartigan–Wong Algorithm. |
|---|---|
| Authors: | Nigro, Libero, Cicirelli, Franco |
| Source: | Computers (2073-431X); Jun2026, Vol. 15 Issue 6, p394, 24p |
| Subject Terms: | Clustering algorithms, K-means clustering, Genetic algorithms, Mathematical optimization, Parallel programming, Data mining |
| Abstract: | The work described in this paper continues basic research aimed at improving clustering algorithms such as K-Means and Random Swap through careful seeding and genetic concepts. This paper, in particular, develops a variation in the Hartigan–Wong (HW) algorithm, which, although computationally more expensive, is recognized as a better solution than K-Means. The new algorithm is named Recombinator Hartigan–Wong (Rec-HW). Rec-HW first builds a population of candidate solutions, each tailored to the minimization of the Sum-of-Squared-Errors (SSE) objective function cost. Candidate solutions are then systematically recombined by exploiting the standard behaviour of HW, which performs crossover and mutation operations. Recombinations, as experimentally confirmed, reduce the number of iterations required by basic HW and tend to favour the emergence of a solution close to the optimal one. The paper describes the design of Rec-HW, whose current implementation depends on parallel Java. Good clustering performance is demonstrated by using both benchmark and real-world datasets. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Clustering Performance of a Recombinator Hartigan–Wong Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nigro%2C+Libero%22">Nigro, Libero</searchLink><br /><searchLink fieldCode="AR" term="%22Cicirelli%2C+Franco%22">Cicirelli, Franco</searchLink> – Name: TitleSource Label: Source Group: Src Data: Computers (2073-431X); Jun2026, Vol. 15 Issue 6, p394, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The work described in this paper continues basic research aimed at improving clustering algorithms such as K-Means and Random Swap through careful seeding and genetic concepts. This paper, in particular, develops a variation in the Hartigan–Wong (HW) algorithm, which, although computationally more expensive, is recognized as a better solution than K-Means. The new algorithm is named Recombinator Hartigan–Wong (Rec-HW). Rec-HW first builds a population of candidate solutions, each tailored to the minimization of the Sum-of-Squared-Errors (SSE) objective function cost. Candidate solutions are then systematically recombined by exploiting the standard behaviour of HW, which performs crossover and mutation operations. Recombinations, as experimentally confirmed, reduce the number of iterations required by basic HW and tend to favour the emergence of a solution close to the optimal one. The paper describes the design of Rec-HW, whose current implementation depends on parallel Java. Good clustering performance is demonstrated by using both benchmark and real-world datasets. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Computers (2073-431X) is the property of MDPI 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: 10.3390/computers15060394 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 394 Subjects: – SubjectFull: Clustering algorithms Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Parallel programming Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: Clustering Performance of a Recombinator Hartigan–Wong Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nigro, Libero – PersonEntity: Name: NameFull: Cicirelli, Franco IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2073431X Numbering: – Type: volume Value: 15 – Type: issue Value: 6 Titles: – TitleFull: Computers (2073-431X) Type: main |
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