Academic Journal
Genetic Elitist Approach and Density Peaks to Improve K-Means Clustering.
| Title: | Genetic Elitist Approach and Density Peaks to Improve K-Means Clustering. |
|---|---|
| Authors: | Nigro, Libero, Cicirelli, Franco, Pupo, Francesco |
| Source: | Algorithms; Feb2026, Vol. 19 Issue 2, p131, 20p |
| Subject Terms: | K-means clustering, Genetic algorithms, Data analysis, K-nearest neighbor classification, Clustering algorithms, Cluster analysis (Statistics), Optimization algorithms |
| Abstract: | K-Means is a well-known algorithm for unsupervised clustering, very often used due to its simplicity and efficiency. Its long-time widespread use has stimulated researchers to investigate its properties further. A critical property concerns K-Means's strong dependence on the seeding method adopted to initialize centroids. Poor initialization causes K-Means to get stuck in a local sub-optimal solution. This paper proposes DPCCs—Density Peaks of Candidate Centroids—a novel seeding method for K-Means. DPCC rests on genetic concepts and density peaks to define an initialization solution close to the optimal one. First, a population of J elitist candidate solutions, that is, solutions capable of yielding a reduced clustering cost, is built. Although none of these particular solutions can be near the optimal one, candidate centroids, as experimentally confirmed, tend to thicken around ground truth centroids. Therefore, subsequent generations of the population are created by repeating the k-nearest neighbors (kNNs) procedure for different values of the k parameter, and estimating density through the reverse nearest neighbors (RNNs) relationship of each centroid. Centroid density peaks are then exploited to rearrange the population solutions toward extracting a candidate solution, which is finally optimized by K-Means. The paper describes the design and operation of DPCC, which is currently implemented in parallel Java. The clustering effectiveness of DPCC is demonstrated by applications to both benchmark and real-world datasets. Results are compared with those of other competing algorithms. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19994893&ISBN=&volume=19&issue=2&date=20260201&spage=131&pages=131-150&title=Algorithms&atitle=Genetic%20Elitist%20Approach%20and%20Density%20Peaks%20to%20Improve%20K-Means%20Clustering.&aulast=Nigro%2C%20Libero&id=DOI:10.3390/a19020131 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Genetic Elitist Approach and Density Peaks to Improve K-Means Clustering. – 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><br /><searchLink fieldCode="AR" term="%22Pupo%2C+Francesco%22">Pupo, Francesco</searchLink> – Name: TitleSource Label: Source Group: Src Data: Algorithms; Feb2026, Vol. 19 Issue 2, p131, 20p – Name: Subject Label: Subject Terms Group: Su Data: <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="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: K-Means is a well-known algorithm for unsupervised clustering, very often used due to its simplicity and efficiency. Its long-time widespread use has stimulated researchers to investigate its properties further. A critical property concerns K-Means's strong dependence on the seeding method adopted to initialize centroids. Poor initialization causes K-Means to get stuck in a local sub-optimal solution. This paper proposes DPCCs—Density Peaks of Candidate Centroids—a novel seeding method for K-Means. DPCC rests on genetic concepts and density peaks to define an initialization solution close to the optimal one. First, a population of J elitist candidate solutions, that is, solutions capable of yielding a reduced clustering cost, is built. Although none of these particular solutions can be near the optimal one, candidate centroids, as experimentally confirmed, tend to thicken around ground truth centroids. Therefore, subsequent generations of the population are created by repeating the k-nearest neighbors (kNNs) procedure for different values of the k parameter, and estimating density through the reverse nearest neighbors (RNNs) relationship of each centroid. Centroid density peaks are then exploited to rearrange the population solutions toward extracting a candidate solution, which is finally optimized by K-Means. The paper describes the design and operation of DPCC, which is currently implemented in parallel Java. The clustering effectiveness of DPCC is demonstrated by applications to both benchmark and real-world datasets. Results are compared with those of other competing algorithms. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Algorithms 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/a19020131 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 131 Subjects: – SubjectFull: K-means clustering Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Data analysis Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Optimization algorithms Type: general Titles: – TitleFull: Genetic Elitist Approach and Density Peaks to Improve K-Means Clustering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nigro, Libero – PersonEntity: Name: NameFull: Cicirelli, Franco – PersonEntity: Name: NameFull: Pupo, Francesco IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19994893 Numbering: – Type: volume Value: 19 – Type: issue Value: 2 Titles: – TitleFull: Algorithms Type: main |
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