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Clustering dense graphs: A web site graph paradigm

Clustering dense graphs: A web site graph paradigm. Author ： L. Moussiades , A. Vakali Presented ： Fen-Rou Ciou IPM, 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Clustering dense graphs: A web site graph paradigm

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  1. Clustering dense graphs: A web site graph paradigm Author ：L. Moussiades, A. Vakali Presented ： Fen-Rou Ciou IPM, 2010

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • A conventional cluster number of links connected a vertex to its cluster is higher than the number of links connected the vertex to the remaining graph.

  4. Objectives • To propose a graph-clustering algorithm is proved a refined cluster are more strongly connected with their cluster than with any other cluster.

  5. Methodology Max • Schematic diagram

  6. Methodology • Basic definition and notations

  7. Methodology • Basic definition and notations

  8. Methodology • Criterion function ICR

  9. Methodology • Algorithm AICR

  10. Experiments Artificial Data

  11. Experiments Purity for clustering solutions

  12. Experiments • Amod on ds1 and ds9 • AICR on ds1 and ds9

  13. Experiments • csd site graph • Singular site graph Amod AICR

  14. Experiments Number of clusters

  15. Experiments • AICR • AMod

  16. Conclusions • A novel graph-clustering algorithm is efficient in the exploration of densely interconnected clusters. • A refine clusters may be more densely interconnect than conventional ones.

  17. Comments • Advantages • It's efficient for densely interconnected datasets. • Applications • Hierarchical agglomerative Clustering

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