A fair-multicluster approach to clustering of categorical data

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Santos Mangudo, Carlos and Heras Martínez, Antonio José (2022) A fair-multicluster approach to clustering of categorical data. Central European Journal of Operations Research . ISSN 1435-246X

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Official URL: https://doi.org/10.1007/s10100-022-00824-2



Abstract

In the last few years, the need of preventing classification biases due to race, gender, social status, etc. has increased the interest in designing fair clustering algorithms. The main idea is to ensure that the output of a cluster algorithm is not biased towards or against specific subgroups of the population. There is a growing specialized literature on this topic, dealing with the problem of clustering numerical data bases. Nevertheless, to our knowledge, there are no previous papers devoted to the problem of fair clustering of pure categorical attributes. In this paper, we show that the Multicluster methodology proposed by Santos and Heras (Interdiscip J Inf Knowl Manag 15:227–246, 2020. https://doi.org/10.28945/4643) for clustering categorical data, can be modified in order to increase the fairness of the clusters. Of course, there is a tradeoff between fairness and efficiency, so that an increase in the fairness objective usually leads to a loss of classification efficiency. Yet it is possible to reach a reasonable compromise between these goals, since the methodology proposed by Santos and Heras (2020) can be easily adapted in order to get homogeneous and fair clusters.


Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2022)

Uncontrolled Keywords:Clustering; Fairness; Fair clustering; Categorical data
Subjects:Sciences > Statistics
ID Code:75675
Deposited On:22 Nov 2022 12:51
Last Modified:22 Nov 2022 13:03

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