Fair-Capacitated Clustering [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Quy, Tai Le; Roy, Arjun; Friege, Gunnar; Ntoutsi, Eirini
Titel	Fair-Capacitated Clustering [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Cluster Grouping; Artificial Intelligence; Mathematics; Computer Uses in Education; Grouping (Instructional Purposes); Foreign Countries; Secondary School Students; College Students; Portugal; United Kingdom (England); Massachusetts (Cambridge) + Suchen Sie Ihr Suchwort? Eingruppierung; Künstliche Intelligenz; Mathematik; Computernutzung; Grouping; Gruppenbildung; Ausland; Sekundarschüler; Collegestudent
Abstract	Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make the clusters useful for the end-user, a "balanced cardinality" among the clusters is required. Our motivation comes from the education domain where studies indicate that students might learn better in diverse student groups and of course groups of similar cardinality are more practical e.g., for group assignments. To this end, we introduce the "fair-capacitated clustering problem" that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: (1) we rely on fairlets to generate minimal sets that satisfy the fair constraint; and (2) we propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering. Our experiments on three educational datasets show that our approaches deliver well-balanced clusters in terms of both fairness and cardinality while maintaining a good clustering quality. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01