Summary of Interpretable Clustering: a Survey, by Lianyu Hu et al.
Interpretable Clustering: A Survey
by Lianyu Hu, Mudi Jiang, Junjie Dong, Xinying Liu, Zengyou He
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 provides a comprehensive review of explainable clustering algorithms, focusing on enhancing transparency and interpretability. It identifies key criteria to distinguish between various methods, allowing researchers to select suitable approaches for specific applications. The goal is to promote the development and adoption of clustering algorithms that balance efficiency with transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make clustering algorithms more transparent and easy to understand. This is important because these algorithms are being used in high-stakes areas like healthcare, finance, and self-driving cars. To gain trust and comply with ethical and regulatory demands, decisions made by these algorithms need to be clear and justified. The paper reviews existing methods for explainable clustering and identifies what makes each one unique. This helps researchers choose the best approach for their specific project. |
Keywords
» Artificial intelligence » Clustering
