Summary of Scalable Density-based Clustering with Random Projections, by Haochuan Xu et al.
Scalable Density-based Clustering with Random Projections
by Haochuan Xu, Ninh Pham
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents a novel scalable density-based clustering algorithm called sDBSCAN that can efficiently identify core points and their neighborhoods in high-dimensional spaces with cosine distance. By leveraging the neighborhood-preserving property of random projections, sDBSCAN can quickly output a clustering structure similar to DBSCAN under mild conditions with high probability. The authors also introduce sOPTICS, a scalable version of OPTICS for interactive exploration of the intrinsic clustering structure. Furthermore, they extend sDBSCAN and sOPTICS to various distances (L2, L1, χ^2, and Jensen-Shannon) using random kernel features. Empirically, sDBSCAN outperforms other clustering algorithms in terms of speed and accuracy on large-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to group similar things together called sDBSCAN. It’s like finding groups of friends at school – you can quickly see who hangs out with whom! The authors also came up with a way to make this process faster and more interactive, which is important for exploring big datasets. They even showed that their method works well with different types of distances between things. This new approach is much faster and accurate than other methods on really large datasets. |
Keywords
* Artificial intelligence * Clustering * Probability
