Academic Journal

An Exact Solution Approach for Hierarchical Clustering.

Bibliographic Details
Title: An Exact Solution Approach for Hierarchical Clustering.
Authors: Willemsen, Rick1 (AUTHOR) willemsen@ese.eur.nl, Cavicchia, Carlo1 (AUTHOR) cavicchia@ese.eur.nl, van den Heuvel, Wilco1 (AUTHOR) wvandenheuvel@ese.eur.nl, van de Velden, Michel1 (AUTHOR) vandevelden@ese.eur.nl
Source: INFORMS Journal on Computing. Mar/Apr2026, Vol. 38 Issue 2, p447-462. 16p.
Subject Terms: *Mathematical programming, *Mathematical optimization, Hierarchical clustering (Cluster analysis), Mixed integer linear programming, Analytical solutions, Cost functions, Clustering algorithms
Abstract: In hierarchical clustering, a hierarchy of nested data partitions is obtained. Commonly used agglomerative and divisive heuristics do not optimize over a global objective function. Although several objective functions and approximation algorithms have been proposed, exact methods that find optimal solutions based on these objective functions have received little attention. In this paper, we consider an objective function involving a sum of partitional clustering objectives over each level. We introduce two compact mixed-integer linear programming formulations as well as a set-covering formulation that can handle various objective functions. In addition, we provide a branch-and-price framework to solve the set-covering formulation. We apply our branch-and-price approach to real-world data instances containing up to 200 observations compared with 15 in the literature. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0903) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0903). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Exact Solution Approach for Hierarchical Clustering.
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  Data: <searchLink fieldCode="AR" term="%22Willemsen%2C+Rick%22">Willemsen, Rick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> willemsen@ese.eur.nl</i><br /><searchLink fieldCode="AR" term="%22Cavicchia%2C+Carlo%22">Cavicchia, Carlo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cavicchia@ese.eur.nl</i><br /><searchLink fieldCode="AR" term="%22van+den+Heuvel%2C+Wilco%22">van den Heuvel, Wilco</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wvandenheuvel@ese.eur.nl</i><br /><searchLink fieldCode="AR" term="%22van+de+Velden%2C+Michel%22">van de Velden, Michel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vandevelden@ese.eur.nl</i>
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  Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Computing%22">INFORMS Journal on Computing</searchLink>. Mar/Apr2026, Vol. 38 Issue 2, p447-462. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+clustering+%28Cluster+analysis%29%22">Hierarchical clustering (Cluster analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Analytical+solutions%22">Analytical solutions</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink>
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  Data: In hierarchical clustering, a hierarchy of nested data partitions is obtained. Commonly used agglomerative and divisive heuristics do not optimize over a global objective function. Although several objective functions and approximation algorithms have been proposed, exact methods that find optimal solutions based on these objective functions have received little attention. In this paper, we consider an objective function involving a sum of partitional clustering objectives over each level. We introduce two compact mixed-integer linear programming formulations as well as a set-covering formulation that can handle various objective functions. In addition, we provide a branch-and-price framework to solve the set-covering formulation. We apply our branch-and-price approach to real-world data instances containing up to 200 observations compared with 15 in the literature. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0903) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0903). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1287/ijoc.2024.0903
    Languages:
      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 447
    Subjects:
      – SubjectFull: Mathematical programming
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Hierarchical clustering (Cluster analysis)
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Analytical solutions
        Type: general
      – SubjectFull: Cost functions
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
    Titles:
      – TitleFull: An Exact Solution Approach for Hierarchical Clustering.
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            NameFull: Willemsen, Rick
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            NameFull: Cavicchia, Carlo
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            NameFull: van den Heuvel, Wilco
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            – D: 01
              M: 03
              Text: Mar/Apr2026
              Type: published
              Y: 2026
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              Value: 38
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