Summary of Fair Clustering For Data Summarization: Improved Approximation Algorithms and Complexity Insights, by Ameet Gadekar et al.
Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights
by Ameet Gadekar, Aristides Gionis, Suhas Thejaswi
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Discrete Mathematics (cs.DM)
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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 research paper explores fair data summarization as a k-supplier problem, where the goal is to select a minimum number of cluster centers from predefined subsets, while minimizing the maximum distance between any data point and its nearest center. The study focuses on two variants: disjoint groups and overlapping groups, each with distinct computational complexity. The authors aim to develop efficient algorithms for solving this fair k-supplier problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to summarize a big dataset into smaller groups. This is like clustering! But what if some of these groups are related? That’s where the concept of “fairness” comes in. In this study, researchers want to find the best way to summarize data into clusters while making sure each group gets a fair share of representation. |
Keywords
» Artificial intelligence » Clustering » Summarization
