Contrastive Explainable Clustering with Differential Privacy

• URL: http://arxiv.org/abs/2406.04610v2
• Date: Sun, 01 Jun 2025 00:38:47 GMT
• Title: Contrastive Explainable Clustering with Differential Privacy
• Authors: Dung Nguyen, Ariel Vetzler, Sarit Kraus, Anil Vullikanti,
• Abstract summary: This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms.<n>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.<n>Our key contribution is demonstrating that these differentially private explanations achieve essentially the same utility bounds as non-private explanations.
• Score: 25.19112971690494
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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