Summary of Counterfactual Explanations For Clustering Models, by Aurora Spagnol et al.
Counterfactual Explanations for Clustering Models
by Aurora Spagnol, Kacper Sokol, Pietro Barbiero, Marc Langheinrich, Martin Gjoreski
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 Clustering algorithms rely on complex optimization processes that may be difficult to comprehend, especially for individuals who lack technical expertise. The paper proposes a new, model-agnostic technique for explaining clustering algorithms with counterfactual statements. This approach relies on a novel soft-scoring method that captures the spatial information utilized by clustering models and builds upon a state-of-the-art Bayesian counterfactual generator for supervised learning to deliver high-quality explanations. The performance of this approach is evaluated on five datasets and two clustering algorithms, demonstrating improved results when introducing soft scores to guide counterfactual search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised machine learning, especially clustering, can be tricky to understand even for those with technical expertise. This paper tries to make it easier by proposing a new way to explain how clustering works using something called “counterfactual statements”. It uses a special kind of scoring method that helps us understand where clusters are coming from and builds on a well-known technique used in supervised learning. The results show that this approach works better when we use soft scores to guide the search for explanations. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Optimization » Supervised » Unsupervised
