# Contrastive Explainable Clustering with Differential Privacy

arXiv: https://arxiv.org/abs/2406.04610 · HTML: https://mlsanity.com/paper/2406.04610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 23 of 23)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering](https://mlsanity.com/paper/1811.03195/) (2018)
- [Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences](https://mlsanity.com/paper/1807.08706/) (2018)
- [Differentially Private Clustering: Tight Approximation Ratios](https://mlsanity.com/paper/2008.08007/) (2020)
- [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations](https://mlsanity.com/paper/2002.01080/) (2020)
- [Model Explanations with Differential Privacy](https://mlsanity.com/paper/2006.09129/) (2020)
- [Differentially Private k-Means with Constant Multiplicative Error](https://mlsanity.com/paper/1804.08001/) (2018)
- [Private estimation algorithms for stochastic block models and mixture models](https://mlsanity.com/paper/2301.04822/) (2023)
- [The privacy issue of counterfactual explanations: explanation linkage attacks](https://mlsanity.com/paper/2210.12051/) (2022)
- [Clustering Algorithms for the Centralized and Local Models](https://mlsanity.com/paper/1707.04766/) (2017)
- [Locally Private k-Means Clustering](https://mlsanity.com/paper/1907.02513/) (2019)
- [Fast K-Means with Accurate Bounds](https://mlsanity.com/paper/1602.02514/) (2016)
- [Toward Policy Explanations for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2204.12568/) (2022)
- [Explainable Reinforcement Learning via Model Transforms](https://mlsanity.com/paper/2209.12006/) (2022)
- [XRand: Differentially Private Defense against Explanation-Guided Attacks](https://mlsanity.com/paper/2212.04454/) (2022)
- [Explainable Multi-Agent Reinforcement Learning for Temporal Queries](https://mlsanity.com/paper/2305.10378/) (2023)
- [Where to locate COVID-19 mass vaccination facilities?](https://mlsanity.com/paper/2102.07309/) (2021)
- [Explaining Preference-driven Schedules: the EXPRES Framework](https://mlsanity.com/paper/2203.08895/) (2022)
- [Contrastive Explanations of Centralized Multi-agent Optimization Solutions](https://mlsanity.com/paper/2308.05984/) (2023)
- [Differentially private exact recovery for stochastic block models](https://mlsanity.com/paper/2406.02644/) (2024)
- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)

## Built on by (most cited first, 1 of 1)

- [Metric Embedding Initialization-Based Differentially Private and Explainable Graph Clustering](https://mlsanity.com/paper/2509.06214/) (2025)

## Abstract (arXiv)

This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms. Focusing on k-median and k-means problems, we calculate contrastive explanations as the utility difference between original clustering and clustering with a centroid fixed to a specific data point. This method provides personalized insights into centroid placement. Our key contribution is demonstrating that these differentially private explanations achieve essentially the same utility bounds as non-private explanations. Experiments across various datasets show that our approach offers meaningful, privacy-preserving, and individually relevant explanations without significantly compromising clustering utility. This work advances privacy-aware machine learning by balancing data protection, explanation quality, and personalization in clustering tasks.