Semi-Supervised Clustering Algorithms for Grouping Scientific Articles

Diego Vallejo-Huanga, Paulina Morillo, Cèsar Ferri

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

14 Citas (Scopus)

Resumen

Creating sessions in scientific conferences consists in grouping papers with common topics taking into account the size restrictions imposed by the conference schedule. Therefore, this problem can be considered as semi-supervised clustering of documents based on their content. This paper aims to propose modifications in traditional clustering algorithms to incorporate size constraints in each cluster. Specifically, two new algorithms are proposed to semi-supervised clustering, based on: binary integer linear programming with cannot-link constraints and a variation of the K-Medoids algorithm, respectively. The applicability of the proposed semi-supervised clustering methods is illustrated by addressing the problem of automatic configuration of conference schedules by clustering articles by similarity. We include experiments, applying the new techniques, over real conferences datasets: ICMLA-2014, AAAI-2013 and AAAI-2014. The results of these experiments show that the new methods are able to solve practical and real problems.

Idioma originalInglés
Páginas (desde-hasta)325-334
Número de páginas10
PublicaciónProcedia Computer Science
Volumen108
DOI
EstadoPublicada - 2017
Publicado de forma externa
EventoInternational Conference on Computational Science ICCS 2017 - Zurich, Suiza
Duración: 12 jun. 201714 jun. 2017

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