Gorantla, S and Gowda, KN and Deshpande, A and Louis, A (2023) Socially Fair Center-Based and Linear Subspace Clustering. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, 18-22 September 2023, Turin, pp. 727-742.
Full text not available from this repository. (Request a copy)Abstract
Center-based clustering (e.g., k-means, k-medians) and clustering using linear subspaces are the two most popular objectives for partitioning real-world data into smaller clusters. Both these objectives minimize the average cost of clustering over all the points. However, when the points belong to different sensitive demographic groups and the optimal clustering has a significantly different cost per point for different groups, it can cause fairness-related harms (e.g., different quality-of-service). To mitigate these harms, the socially fair clustering objective minimizes the cost of clustering per point for the worst-off group. In this work, we propose a unified framework to solve socially fair center-based and linear subspace clustering and give practical and efficient approximation algorithms for these problems. We perform extensive experiments to show that our algorithms closely match or outperform existing baselines on multiple benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to the Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Approximation algorithms; Data mining; K-means clustering; Machine learning, Average cost; Based clustering; Center-based; Clusterings; K-means; K-median; Linear subspace; Real-world; Small clusters; Subspace clustering, Quality of service |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 17 Dec 2023 09:55 |
| Last Modified: | 17 Dec 2023 09:55 |
| URI: | https://eprints.iisc.ac.in/id/eprint/83459 |
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