Radius-Guided Post-Clustering for Shape-Aware, Scalable Refinement of k-Means Results

• URL: http://arxiv.org/abs/2504.20293v1
• Date: Mon, 28 Apr 2025 22:30:53 GMT
• Title: Radius-Guided Post-Clustering for Shape-Aware, Scalable Refinement of k-Means Results
• Authors: Stefan Kober,
• Abstract summary: After standard k-means, each cluster center is assigned a radius (the distance to its assigned point), and clusters whose radii overlap are merged.<n>This post-processing step loosens the requirement for exact k long as k is.<n>The method can often reconstruct non-estimated shapes over meaningful merges.
• Score: 1.9580473532948401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional k-means clustering underperforms on non-convex shapes and requires the number of clusters k to be specified in advance. We propose a simple geometric enhancement: after standard k-means, each cluster center is assigned a radius (the distance to its farthest assigned point), and clusters whose radii overlap are merged. This post-processing step loosens the requirement for exact k: as long as k is overestimated (but not excessively), the method can often reconstruct non-convex shapes through meaningful merges. We also show that this approach supports recursive partitioning: clustering can be performed independently on tiled regions of the feature space, then globally merged, making the method scalable and suitable for distributed systems. Implemented as a lightweight post-processing step atop scikit-learn's k-means, the algorithm performs well on benchmark datasets, achieving high accuracy with minimal additional computation.

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