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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
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