Summary of Provable Imbalanced Point Clustering, by David Denisov et al.
Provable Imbalanced Point Clustering
by David Denisov, Dan Feldman, Shlomi Dolev, Michael Segal
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes efficient and provable methods to approximate imbalanced point clustering, fitting k-centers to a set of points in Rd for any d,k≥1. The authors utilize coresets, weighted sets of points that approximate the fitting loss for every model up to a multiplicative factor of 1±ε. They provide experiments showing the empirical contribution of their methods on real images, synthetic data, and real-world data. Additionally, they propose choice clustering, which combines clustering algorithms to achieve better performance than each one separately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds new ways to group similar points together in high-dimensional spaces. It develops efficient methods for fitting k-centers to these points, even when there are more “negative” points than “positive” ones. The authors test their methods on real images, fake data, and real-world datasets. They show that by combining different clustering algorithms, they can get better results. |
Keywords
» Artificial intelligence » Clustering » Synthetic data
