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A Literature Review on Correlation Clustering: Cross-disciplinary Taxonomy with Bibliometric Analysis

Author

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  • Dewan F. Wahid

    (McMaster University)

  • Elkafi Hassini

    (McMaster University
    McMaster University)

Abstract

The correlation clustering problem identifies clusters in a set of objects when the qualitative information about objects’ mutual similarities or dissimilarities is given in a signed network. This clustering problem has been studied in different scientific areas, including computer sciences, operations research, and social sciences. A plethora of applications, problem extensions, and solution approaches have resulted from these studies. This paper focuses on the cross-disciplinary evolution of this problem by analysing the taxonomic and bibliometric developments during the 1992 to 2020 period. With the aim of enhancing cross-fertilization of knowledge, we present a unified discussion of the problem, including details of several mathematical formulations and solution approaches. Additionally, we analyse the literature gaps and propose some dominant research directions for possible future studies.

Suggested Citation

  • Dewan F. Wahid & Elkafi Hassini, 2022. "A Literature Review on Correlation Clustering: Cross-disciplinary Taxonomy with Bibliometric Analysis," SN Operations Research Forum, Springer, vol. 3(3), pages 1-42, September.
    • Handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00156-6
    DOI: 10.1007/s43069-022-00156-6
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    References listed on IDEAS

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