Summary of An Axiomatic Definition Of Hierarchical Clustering, by Ery Arias-castro and Elizabeth Coda
An Axiomatic Definition of Hierarchical Clustering
by Ery Arias-Castro, Elizabeth Coda
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 takes an axiomatic approach to developing a hierarchical clustering model for piecewise constant densities, extending this concept to more general densities. The proposed method is similar to Lebesgue integration and can be applied when the density meets certain conditions, such as having connected support or being continuous. Under these conditions, the model results in Hartigan’s cluster tree definition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to group things together based on their properties. It creates a new way of doing this that works for lots of different types of data. The method is similar to how we add up small pieces of something (like areas) to get the total. If the data has certain characteristics, like being connected or smooth, then our new approach produces a special type of grouping called a cluster tree. |
Keywords
» Artificial intelligence » Hierarchical clustering
