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Found 5 papers, 2 papers with code
Explainable k-Means and k-Medians Clustering
In terms of negative results, we show that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and we prove that any explainable clustering must incur an \Omega(\log k) approximation compared to the optimal clustering.
Framework for Evaluating Faithfulness of Local Explanations
We study the faithfulness of an explanation system to the underlying prediction model.
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel.
ExKMC: Expanding Explainable $k$-Means Clustering
To allow flexibility, we develop a new explainable $k$-means clustering algorithm, ExKMC, that takes an additional parameter $k' \geq k$ and outputs a decision tree with $k'$ leaves.
Explainable $k$-Means and $k$-Medians Clustering
In terms of negative results, we show, first, that popular top-down decision tree algorithms may lead to clusterings with arbitrarily large cost, and second, that any tree-induced clustering must in general incur an $\Omega(\log k)$ approximation factor compared to the optimal clustering.
