Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

Two Approaches for Graph Clustering

  • Two approaches for clustering graph data
    • Use generic clustering methods for high-dimensional data
    • Designed specifically for clustering graphs
  • Using clustering methods for high-dimensional data
    • Extract a similarity matrix from a graph using a similarity measure
    • A generic clustering method can then be applied on the similarity matrix to discover clusters
    • Ex. Spectral clustering: approximate optimal graph cut solutions
  • Methods specific to graphs
    • Search the graph to find well-connected components as clusters
    • Ex. SCAN (Structural Clustering Algorithm for Networks)
      • X. Xu, N. Yuruk, Z. Feng, and T. A. J. Schweiger, “SCAN: A Structural Clustering Algorithm for Networks”, KDD'07

Speaker notes:

Content Tools


There are currently no sources for this slide.