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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Vydáno v:IEEE access Ročník 13; s. 67693 - 67717
Hlavní autoři: Rodriguez Corominas, Guillem, Blesa, Maria J., Blum, Christian
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Approximation algorithms
Clustering
Clustering algorithms
Computational efficiency
D² sampling
Filters
k-means
Machine learning algorithms
Measurement
norm
Prediction algorithms
Scalability
triangle inequality
Vectors
ISSN:2169-3536, 2169-3536
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Shrnutí: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3561293