# XClusters: Explainability-first Clustering

arXiv: https://arxiv.org/abs/2209.10956 · HTML: https://mlsanity.com/paper/2209.10956/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 15 of 15)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Deep Lattice Networks and Partial Monotonic Functions](https://mlsanity.com/paper/1709.06680/) (2017)
- [Monotonic Calibrated Interpolated Look-Up Tables](https://mlsanity.com/paper/1505.06378/) (2015)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [On the price of explainability for some clustering problems](https://mlsanity.com/paper/2101.01576/) (2021)
- [Nearly-Tight and Oblivious Algorithms for Explainable Clustering](https://mlsanity.com/paper/2106.16147/) (2021)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Shallow decision trees for explainable k-means clustering](https://mlsanity.com/paper/2112.14718/) (2021)
- [Near-optimal Algorithms for Explainable k-Medians and k-Means](https://mlsanity.com/paper/2107.00798/) (2021)
- [Mixed Monotonic Programming for Fast Global Optimization](https://mlsanity.com/paper/1910.07853/) (2019)

## Built on by (most cited first, 2 of 2)

- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)
- [Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention](https://mlsanity.com/paper/2405.04854/) (2024)

## Abstract (arXiv)

We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is a difference of monotonic functions. We then propose an efficient branch-and-bound algorithm for finding the best parameters that lead to a balance of cluster distortion and decision tree explainability. Our experiments show that our method can improve the explainability of any clustering that fits in our framework.