Accelerating the k-Means++ Algorithm by Using Geometric Information
Year: 2025
Type: article
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 exactk-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 standardk-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.
Source: IEEE Access
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: G1175115578, G1983892709, G2064873908, G2239323741, G2262748287, G2309174360, G2322467035, G2355262757, G2660406951, G288263039, G3005324705, G300979063, G34346518, G3480869486, G3518538256, G4187728303, G425668148, G4749977292, G5080475149, G5338730000, G546199279, G5470535847, G5852091828, G6218905885, G69051668, G6987569140, G7084143925, G7847464687, G8888022036, G8920660035
OpenAlex ID: W4409494232