# SPORE: Skeleton Propagation Over Recalibrating Expansions

arXiv: https://arxiv.org/abs/2511.00064 · HTML: https://mlsanity.com/paper/2511.00064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 72 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Learning Discrete Representations via Information Maximizing Self-Augmented Training](https://mlsanity.com/paper/1702.08720/) (2017)

## Abstract (arXiv)

Many real-world datasets are not linearly separable, limiting the effectiveness of centroid-based clustering methods such as K-means. Density-based clustering methods address this limitation by identifying clusters with arbitrary geometric structure; however, existing approaches exhibit two persistent shortcomings. First, they often underperform in the presence of heterogeneous local densities, where a single density threshold cannot adequately capture clusters across multiple density scales. Second, they generally lack the clear boundary delineation naturally induced by the linear partitioning mechanism of centroid-based methods. This paper introduces SPORE (Skeleton Propagation Over Recalibrating Expansions), a clustering algorithm designed to address both challenges while preserving the geometric flexibility of density-based approaches. SPORE operates in two stages: an adaptive cluster expansion phase followed by a proximity-driven boundary propagation phase that maintains discriminative capability even under weak density contrast. The proposed method is evaluated on 28 benchmark datasets against established density-based baselines, with K-means included as a reference centroid-based method. Experimental results demonstrate that SPORE achieves significantly improved cluster recovery relative to all evaluated baselines (p < 0.01), while strong-performing configurations can be identified within five random-search evaluations.