Eigenvectors for clustering: Unipartite, bipartite, and directed graph cases

@article{Mirzal2010EigenvectorsFC,
  title={Eigenvectors for clustering: Unipartite, bipartite, and directed graph cases},
  author={Andri Mirzal and Masashi Furukawa},
  journal={2010 International Conference on Electronics and Information Engineering},
  year={2010},
  volume={1},
  pages={V1-303-V1-309}
}
This paper presents a concise tutorial on spectral clustering for broad spectrum graphs which include unipartite (undirected) graph, bipartite graph, and directed graph. We show how to transform bipartite graph and directed graph into corresponding unipartite graph, therefore allowing a unified treatment to all cases. In bipartite graph, we show that the relaxed solution to the K-way co-clustering can be found by computing the left and right eigenvectors of the data matrix. This gives a… CONTINUE READING

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