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Inhomogeneous Hypergraph Clustering with Applications
[article]
2017
arXiv
pre-print
Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partitioning hyperedges across clusters. Algorithmic solutions based on this approach assume that different partitions of a hyperedge incur the same cost. However, this assumption fails to leverage the fact that different subsets of vertices within the same hyperedge may have different
arXiv:1709.01249v4
fatcat:z2fprzxx7ved3ppy3fowgmxh3e