clustering dense graphs: a web site graph paradigm

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Clustering dense graphs: A web site graph paradigm

Author ： L. Moussiades, A. Vakali

Presented ： Fen-Rou Ciou

IPM, 2010


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Outlines

· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments


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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.


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Objectives

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


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I. M.Methodology Schematic diagram

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Max


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I. M.Methodology Basic definition and notations

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I. M.Methodology Basic definition and notations

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I. M.Methodology Criterion function ICR

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I. M.Methodology Algorithm AICR

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· Artificial Data

Experiments

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· Purity for clustering solutions

Experiments

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I. M.Experiments· Amod on ds1 and ds9 · AICR on ds1 and ds9

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I. M.Experiments· csd site graph · Singular site graph

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Amod

AICR


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· Number of clusters

Experiments

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I. M.Experiments· AICR · AMod

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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.


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Comments

· Advantages─ It's efficient for densely interconnected datasets.

· Applications─ Hierarchical agglomerative Clustering

clustering dense graphs: a web site graph paradigm

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