Summary of Pandora: a Parallel Dendrogram Construction Algorithm For Single Linkage Clustering on Gpu, by Piyush Sao et al.
PANDORA: A Parallel Dendrogram Construction Algorithm for Single Linkage Clustering on GPU
by Piyush Sao, Andrey Prokopenko, Damien Lebrun-Grandié
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper introduces Pandora, a new parallel algorithm for constructing dendrograms in single-linkage hierarchical clustering. The algorithm is designed to efficiently handle skewed dendrograms, which are common in real-world data. By leveraging the hdbscan method, Pandora improves upon traditional methods like agglomerative and divisive techniques that struggle with parallelization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pandora is a new way to build tree-like structures called dendrograms using a technique called single-linkage hierarchical clustering. This helps us group similar things together in a way that’s efficient and can handle tricky data. The problem with current methods is they don’t work well when the data isn’t organized neatly, which is often the case. |
Keywords
* Artificial intelligence * Hierarchical clustering
