Fair k-center Clustering with Outliers

Daichi Amagata
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:10-18, 2024.

Abstract

The importance of dealing with big data is further increasing, as machine learning (ML) systems obtain useful knowledge from big datasets. However, using all data is practically prohibitive because of the massive sizes of the datasets, so summarizing them by centers obtained from k-center clustering is a promising approach. We have two concerns here. One is fairness, because if the summary does not have some specific groups, subsequent applications may provide unfair results for the groups. The other is the presence of outliers, and if outliers dominate the summary, it cannot be useful. To overcome these concerns, we address the problem of fair k-center clustering with outliers. Although prior works studied the fair k-center clustering problem, they do not consider outliers. This paper yields a linear time algorithm that satisfies the fairness constraint of our problem and probabilistically guarantees the almost 3-approximation bound. Its empirical efficiency and effectiveness are also reported.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-amagata24a, title = { Fair k-center Clustering with Outliers }, author = {Amagata, Daichi}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {10--18}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/amagata24a/amagata24a.pdf}, url = {https://proceedings.mlr.press/v238/amagata24a.html}, abstract = { The importance of dealing with big data is further increasing, as machine learning (ML) systems obtain useful knowledge from big datasets. However, using all data is practically prohibitive because of the massive sizes of the datasets, so summarizing them by centers obtained from k-center clustering is a promising approach. We have two concerns here. One is fairness, because if the summary does not have some specific groups, subsequent applications may provide unfair results for the groups. The other is the presence of outliers, and if outliers dominate the summary, it cannot be useful. To overcome these concerns, we address the problem of fair k-center clustering with outliers. Although prior works studied the fair k-center clustering problem, they do not consider outliers. This paper yields a linear time algorithm that satisfies the fairness constraint of our problem and probabilistically guarantees the almost 3-approximation bound. Its empirical efficiency and effectiveness are also reported. } }
Endnote
%0 Conference Paper %T Fair k-center Clustering with Outliers %A Daichi Amagata %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-amagata24a %I PMLR %P 10--18 %U https://proceedings.mlr.press/v238/amagata24a.html %V 238 %X The importance of dealing with big data is further increasing, as machine learning (ML) systems obtain useful knowledge from big datasets. However, using all data is practically prohibitive because of the massive sizes of the datasets, so summarizing them by centers obtained from k-center clustering is a promising approach. We have two concerns here. One is fairness, because if the summary does not have some specific groups, subsequent applications may provide unfair results for the groups. The other is the presence of outliers, and if outliers dominate the summary, it cannot be useful. To overcome these concerns, we address the problem of fair k-center clustering with outliers. Although prior works studied the fair k-center clustering problem, they do not consider outliers. This paper yields a linear time algorithm that satisfies the fairness constraint of our problem and probabilistically guarantees the almost 3-approximation bound. Its empirical efficiency and effectiveness are also reported.
APA
Amagata, D.. (2024). Fair k-center Clustering with Outliers . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:10-18 Available from https://proceedings.mlr.press/v238/amagata24a.html.

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