A hierarchical clustering algorithm based on the Hungarian method

Jacob Goldberger, Tamir Tassa

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


We propose a novel hierarchical clustering algorithm for data-sets in which only pairwise distances between the points are provided. The classical Hungarian method is an efficient algorithm for solving the problem of minimal-weight cycle cover. We utilize the Hungarian method as the basic building block of our clustering algorithm. The disjoint cycles, produced by the Hungarian method, are viewed as a partition of the data-set. The clustering algorithm is formed by hierarchical merging. The proposed algorithm can handle data that is arranged in non-convex sets. The number of the clusters is automatically found as part of the clustering process. We report an improved performance of our algorithm in a variety of examples and compare it to the spectral clustering algorithm.

Original languageEnglish
Pages (from-to)1632-1638
Number of pages7
JournalPattern Recognition Letters
Issue number11
StatePublished - 1 Aug 2008
Externally publishedYes


  • Graph algorithms
  • Grouping
  • Hierarchical clustering
  • Pairwise clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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