Fairness constraint of Fuzzy C-means Clustering improves clustering fairness

Xu Xia, Zhang Hui, Ynag Chunming, Zhao Xujian, Li Bo
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:113-128, 2021.

Abstract

Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-xia21a, title = {Fairness constraint of Fuzzy C-means Clustering improves clustering fairness}, author = {Xia, Xu and Hui, Zhang and Chunming, Ynag and Xujian, Zhao and Bo, Li}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {113--128}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/xia21a/xia21a.pdf}, url = {https://proceedings.mlr.press/v157/xia21a.html}, abstract = {Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.} }
Endnote
%0 Conference Paper %T Fairness constraint of Fuzzy C-means Clustering improves clustering fairness %A Xu Xia %A Zhang Hui %A Ynag Chunming %A Zhao Xujian %A Li Bo %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-xia21a %I PMLR %P 113--128 %U https://proceedings.mlr.press/v157/xia21a.html %V 157 %X Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.
APA
Xia, X., Hui, Z., Chunming, Y., Xujian, Z. & Bo, L.. (2021). Fairness constraint of Fuzzy C-means Clustering improves clustering fairness. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:113-128 Available from https://proceedings.mlr.press/v157/xia21a.html.

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