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Clustering with Minimum Spanning Trees: How Good Can It Be?

Author

Listed:
  • Marek Gagolewski

    (Systems Research Institute, Polish Academy of Sciences
    Warsaw University of Technology, Faculty of Mathematics and Information Science)

  • Anna Cena

    (Warsaw University of Technology, Faculty of Mathematics and Information Science)

  • Maciej Bartoszuk

    (QED Software)

  • Łukasz Brzozowski

    (Warsaw University of Technology, Faculty of Mathematics and Information Science)

Abstract

Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they are meaningful in low-dimensional partitional data clustering tasks. By identifying the upper bounds for the agreement between the best (oracle) algorithm and the expert labels from a large battery of benchmark data, we discover that MST methods can be very competitive. Next, we review, study, extend, and generalise a few existing, state-of-the-art MST-based partitioning schemes. This leads to some new noteworthy approaches. Overall, the Genie and the information-theoretic methods often outperform the non-MST algorithms such as K-means, Gaussian mixtures, spectral clustering, Birch, density-based, and classical hierarchical agglomerative procedures. Nevertheless, we identify that there is still some room for improvement, and thus the development of novel algorithms is encouraged.

Suggested Citation

  • Marek Gagolewski & Anna Cena & Maciej Bartoszuk & Łukasz Brzozowski, 2025. "Clustering with Minimum Spanning Trees: How Good Can It Be?," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 90-112, March.
    • Handle: RePEc:spr:jclass:v:42:y:2025:i:1:d:10.1007_s00357-024-09483-1
    DOI: 10.1007/s00357-024-09483-1
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