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  • 1.
    Angelova, Milena
    et al.
    Technical University of sofia, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Linde, Peter
    Blekinge Institute of Technology, The Library.
    Lavesson, Niklas
    An Expertise Recommender System based on Data from an Institutional Repository (DiVA)2019In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

  • 2.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Angelova, M.
    Angelova, Milena
    Technical University of Sofia, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Tsiporkova, Elena
    EluciDATA Lab, Sirris, BEL.
    Bipartite Split-Merge Evolutionary Clustering2019In: Lect. Notes Comput. Sci., Springer , 2019, p. 204-223Conference paper (Refereed)
    Abstract [en]

    We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge). © Springer Nature Switzerland AG 2019.

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