PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A hierarchical clustering algorithm based on the Hungarian method
Jacob Goldberger and Tamir Tassa
Pattern Recognition Letters Volume 29, Number 11, pp. 1632-1638, 2008. ISSN 0167-8655


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4409
Deposited By:Jacob Goldberger
Deposited On:13 March 2009