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Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding.

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Τίτλος: Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding.
Συγγραφείς: Nigro, Libero, Cicirelli, Franco
Πηγή: Algorithms; Dec2023, Vol. 16 Issue 12, p572, 25p
Θεματικοί όροι: K-means clustering, Sowing, Centroid, Evolutionary algorithms
Περίληψη: K-Means is a "de facto" standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]
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. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding.
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  Data: <searchLink fieldCode="AR" term="%22Nigro%2C+Libero%22">Nigro, Libero</searchLink><br /><searchLink fieldCode="AR" term="%22Cicirelli%2C+Franco%22">Cicirelli, Franco</searchLink>
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  Data: Algorithms; Dec2023, Vol. 16 Issue 12, p572, 25p
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  Data: <searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Sowing%22">Sowing</searchLink><br /><searchLink fieldCode="DE" term="%22Centroid%22">Centroid</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: K-Means is a "de facto" standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets. [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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        Value: 10.3390/a16120572
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 572
    Subjects:
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Sowing
        Type: general
      – SubjectFull: Centroid
        Type: general
      – SubjectFull: Evolutionary algorithms
        Type: general
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      – TitleFull: Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding.
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              M: 12
              Text: Dec2023
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              Y: 2023
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