Semi-Supervised Clustering Algorithms for Grouping Scientific Articles

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)325-334
Number of pages10
JournalProcedia Computer Science
Volume108
DOIs
StatePublished - 2017
EventInternational Conference on Computational Science ICCS 2017 - Zurich, Switzerland
Duration: 12 Jun 201714 Jun 2017

Bibliographical note

Publisher Copyright:
© 2017 The Authors. Published by Elsevier B.V.

Keywords

  • Clustering with constraints
  • K-Medoids
  • Linear programming
  • Size constraint

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