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Authors: Veselka Boeva 1 ; Milena Angelova 2 and Elena Tsiporkova 3

Affiliations: 1 Computer Science and Engineering Dept., Blekinge Institute of Technology, Karlskrona, Sweden ; 2 Computer Systems and Technologies Dept., Technical University of Sofia, Plovdiv, Bulgaria ; 3 The Collective Center for the Belgian Technological Industry, Brussels, Belgium

ISBN: 978-989-758-350-6

Keyword(s): Data Mining, Evolutionary Clustering, Bipartite Clustering, PubMed Data, Unsupervised Learning.

Abstract: In this article we propose a bipartite correlation clustering technique that can be used to adapt the existing clustering solution to a clustering of newly collected data elements. The proposed technique is supposed to provide the flexibility to compute clusters on a new portion of data collected over a defined time period and to update the existing clustering solution by the computed new one. Such an updating clustering should better reflect the current characteristics of the data by being able to examine clusters occurring in the considered time period and eventually capture interesting trends in the area. For example, some clusters will be updated by merging with ones from newly constructed clustering while others will be transformed by splitting their elements among several new clusters. The proposed clustering algorithm, entitled Split-Merge Evolutionary Clustering, is evaluated and compared to another bipartite correlation clustering technique (PivotBiCluster) on two different c ase studies: expertise retrieval and patient profiling in healthcare. (More)

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Paper citation in several formats:
Boeva, V.; Angelova, M. and Tsiporkova, E. (2019). A Split-Merge Evolutionary Clustering Algorithm.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 337-346. DOI: 10.5220/0007573103370346

@conference{icaart19,
author={Veselka Boeva. and Milena Angelova. and Elena Tsiporkova.},
title={A Split-Merge Evolutionary Clustering Algorithm},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={337-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007573103370346},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Split-Merge Evolutionary Clustering Algorithm
SN - 978-989-758-350-6
AU - Boeva, V.
AU - Angelova, M.
AU - Tsiporkova, E.
PY - 2019
SP - 337
EP - 346
DO - 10.5220/0007573103370346

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