Summary of Linear Programming Based Approximation to Individually Fair K-clustering with Outliers, by Binita Maity et al.
Linear Programming based Approximation to Individually Fair k-Clustering with Outliers
by Binita Maity, Shrutimoy Das, Anirban Dasgupta
First submitted to arxiv on: 14 Dec 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 This paper investigates the development of an individually fair k-means clustering algorithm for datasets containing outliers. The goal is to ensure that each non-outlier point has a center within its nearest neighbors. While some recent works have explored individual fairness in clustering, this research focuses on the presence of outliers specifically for k-means clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sure a type of computer program, called k-means clustering, is fair and doesn’t favor or disfavor certain groups. This is important when there are mistakes or errors (called outliers) in the data being used. The researchers want their algorithm to work well even with these errors present. They’re building on previous work that has looked at fairness in clustering, but this one focuses specifically on k-means and errors. |
Keywords
» Artificial intelligence » Clustering » K means
