Summary of New Bounds on the Cohesion Of Complete-link and Other Linkage Methods For Agglomeration Clustering, by Sanjoy Dasgupta and Eduardo Laber
New bounds on the cohesion of complete-link and other linkage methods for agglomeration clustering
by Sanjoy Dasgupta, Eduardo Laber
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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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 abstract proposes improvements to the existing understanding of linkage methods in hierarchical clustering. Specifically, it focuses on complete-link algorithms and their performance in metric spaces. The study aims to establish more accurate upper bounds on the maximum diameter of the resulting clusters, which can inform clustering analysis and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Linkage methods are used for grouping similar things together. Right now, we don’t know much about how well these methods work. This paper tries to fill that gap by learning more about a type of linkage method called complete-link. It helps us understand what kinds of clusters this method can create and how big they can be. |
Keywords
» Artificial intelligence » Clustering » Hierarchical clustering
