Summary of From Large to Small Datasets: Size Generalization For Clustering Algorithm Selection, by Vaggos Chatziafratis et al.
From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection
by Vaggos Chatziafratis, Ishani Karmarkar, Ellen Vitercik
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper addresses the problem of efficiently selecting a suitable clustering algorithm for a massive dataset. In a semi-supervised setting, where an unknown ground-truth clustering can only be accessed through expensive oracle queries, the goal is to find a clustering algorithm whose output is structurally close to the truth. To achieve this, the authors introduce the concept of size generalization for clustering algorithm accuracy and provide theoretical guarantees for three classic algorithms: single-linkage, k-means++, and a smoothed variant of Gonzalez’s k-centers heuristic. The results demonstrate that, in practice, it is possible to identify the best-performing algorithm on the full dataset by evaluating it on a subsample of as little as 5% of the data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem in machine learning: choosing the right clustering algorithm for a huge amount of data. Imagine having a really big box of different colored balls, and you need to group them into clusters based on their color. The issue is that there are many ways to do this, but some methods are much better than others. This paper shows how to find the best method by looking at just a small part of the data, rather than having to look at everything. |
Keywords
* Artificial intelligence * Clustering * Generalization * K means * Machine learning * Semi supervised
