Accelerating the k-Means++ Algorithm by Using Geometric Information
Clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this...
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| Published in: | IEEE access Vol. 13; pp. 67693 - 67717 |
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| Abstract | Clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this paper, we propose an acceleration of the exact k-means++ algorithm using geometric information, specifically the Triangle Inequality and additional norm filters, along with a two-step sampling procedure. Our experiments demonstrate that the accelerated version outperforms the standard k-means++ version in terms of the number of visited points and distance calculations, achieving greater speedup as the number of clusters increases. The version utilizing the Triangle Inequality is particularly effective for low-dimensional data, while the additional norm-based filter enhances performance in high-dimensional instances with greater norm variance among points. Additional experiments show the behavior of our algorithms when executed concurrently across multiple jobs and examine how memory performance impacts practical speedup. |
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| AbstractList | Clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this paper, we propose an acceleration of the exact k-means++ algorithm using geometric information, specifically the Triangle Inequality and additional norm filters, along with a two-step sampling procedure. Our experiments demonstrate that the accelerated version outperforms the standard k-means++ version in terms of the number of visited points and distance calculations, achieving greater speedup as the number of clusters increases. The version utilizing the Triangle Inequality is particularly effective for low-dimensional data, while the additional norm-based filter enhances performance in high-dimensional instances with greater norm variance among points. Additional experiments show the behavior of our algorithms when executed concurrently across multiple jobs and examine how memory performance impacts practical speedup. |
| Author | Blesa, Maria J. Rodriguez Corominas, Guillem Blum, Christian |
| Author_xml | – sequence: 1 givenname: Guillem orcidid: 0000-0002-3863-2017 surname: Rodriguez Corominas fullname: Rodriguez Corominas, Guillem email: guillem.rodriguez.corominas@upc.edu organization: Department of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain – sequence: 2 givenname: Maria J. surname: Blesa fullname: Blesa, Maria J. organization: Department of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain – sequence: 3 givenname: Christian orcidid: 0000-0002-1736-3559 surname: Blum fullname: Blum, Christian organization: Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain |
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| SubjectTerms | Approximation algorithms Clustering Clustering algorithms Computational efficiency D² sampling Filters k-means Machine learning algorithms Measurement norm Prediction algorithms Scalability triangle inequality Vectors |
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| Title | Accelerating the k-Means++ Algorithm by Using Geometric Information |
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