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Authors: Veselka Boeva 1 ; Milena Angelova 2 ; Niklas Lavesson 1 ; Oliver Rosander 1 and Elena Tsiporkova 3

Affiliations: 1 Blekinge Institute of Technology, Sweden ; 2 Technical University of Sofia Plovdiv Branch, Bulgaria ; 3 The Collective Center for the Belgian Technological Industry, Belgium

Keyword(s): Data Mining, Expert Finding, Health Science, Knowledge Management, Natural Language Processing.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: 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 tech niques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. (More)

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Paper citation in several formats:
Boeva, V.; Angelova, M.; Lavesson, N.; Rosander, O. and Tsiporkova, E. (2018). Evolutionary Clustering Techniques for Expertise Mining Scenarios. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 523-530. DOI: 10.5220/0006630605230530

@conference{icaart18,
author={Veselka Boeva. and Milena Angelova. and Niklas Lavesson. and Oliver Rosander. and Elena Tsiporkova.},
title={Evolutionary Clustering Techniques for Expertise Mining Scenarios},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={523-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006630605230530},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Evolutionary Clustering Techniques for Expertise Mining Scenarios
SN - 978-989-758-275-2
IS - 2184-433X
AU - Boeva, V.
AU - Angelova, M.
AU - Lavesson, N.
AU - Rosander, O.
AU - Tsiporkova, E.
PY - 2018
SP - 523
EP - 530
DO - 10.5220/0006630605230530
PB - SciTePress

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