Post on 20-Jan-2016
description
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
Intelligent Database Systems Lab
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Outlines
· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments
Intelligent Database Systems Lab
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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.
Intelligent Database Systems Lab
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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.
Intelligent Database Systems Lab
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I. M.Methodology Schematic diagram
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Max
Intelligent Database Systems Lab
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I. M.Methodology Basic definition and notations
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Intelligent Database Systems Lab
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I. M.Methodology Basic definition and notations
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Intelligent Database Systems Lab
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I. M.Methodology Criterion function ICR
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Intelligent Database Systems Lab
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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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Intelligent Database Systems Lab
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I. M.Experiments· Amod on ds1 and ds9 · AICR on ds1 and ds9
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Intelligent Database Systems Lab
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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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Intelligent Database Systems Lab
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I. M.Experiments· AICR · AMod
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Intelligent Database Systems Lab
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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.
Intelligent Database Systems Lab
N.Y.U.S.T.
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Comments
· Advantages─ It's efficient for densely interconnected datasets.
· Applications─ Hierarchical agglomerative Clustering