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2008

A hierarchical clustering algorithm based on the Hungarian method

8 years 10 months ago
A hierarchical clustering algorithm based on the Hungarian method
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.
Jacob Goldberger, Tamir Tassa
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PRL
Authors Jacob Goldberger, Tamir Tassa
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