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Evolutionary clustering techniques for expertise mining scenarios
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Technical University Sofia, BUL.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-0535-1761
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
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2018 (English)In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress , 2018, Vol. 2, p. 523-530Conference paper, Published paper (Refereed)
Abstract [en]

The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Place, publisher, year, edition, pages
SciTePress , 2018. Vol. 2, p. 523-530
Keywords [en]
Data Mining, Expert Finding, Health Science, Knowledge Management, Natural Language Processing, Artificial intelligence, Cluster analysis, Natural language processing systems, Search engines, Clustering approach, Clustering solutions, Data elements, Evolutionary clustering, Retrieval systems, System database
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16224Scopus ID: 2-s2.0-85046663632ISBN: 9789897582752 OAI: oai:DiVA.org:bth-16224DiVA, id: diva2:1209796
Conference
10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-24Bibliographically approved

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Boeva, VeselkaLavesson, Niklas

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Citation style
  • apa
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Output format
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