Efficient algorithms for fair clustering with a new notion of fairness

Author:

Gupta ShivamORCID,Ghalme GaneshORCID,Krishnan Narayanan C.ORCID,Jain ShwetaORCID

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Reference85 articles.

1. Abbasi M, Bhaskara A, Venkatasubramanian S (2021) Fair clustering via equitable group representations. In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp 504–514

2. Abraham SS, Padmanabhan D, Sundaram SS (2020) Fairness in clustering with multiple sensitive attributes. In: EDBT/ICDT 2020 joint conference, pp 287–298

3. Ahmadian S, Epasto A, Kumar R, Mahdian M (2019) Clustering without over-representation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 267–275

4. Ahmadian S, Epasto A, Kumar R, Mahdian M (2020) Fair correlation clustering. In: Chiappa S, Calandra R (eds) Proceedings of the twenty third international conference on artificial intelligence and statistics, volume 108 of proceedings of machine learning research, pp 4195–4205

5. Anderson N, Bera SK, Das S, Liu Y (2020) Distributional individual fairness in clustering. arXiv:2006.12589

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