Clique percolation for finding naturally cohesive and overlapping document clusters

Techniques for find document clusters mostly depend on models that impose strong explicit and/or implicit priori assumptions. As a consequence, the clustering effects tend to be unnatural and stray away from the intrinsic grouping natures of a document collection. We apply a novel graph-theoretic te...

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Bibliographic Details
Main Authors: GAO, Wei, WONG, Kam-Fai, XIA, Yunqing, XU, Ruifeng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
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Online Access:https://ink.library.smu.edu.sg/sis_research/4602
https://ink.library.smu.edu.sg/context/sis_research/article/5605/viewcontent/Gao2006_Chapter_CliquePercolationMethodForFind.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Techniques for find document clusters mostly depend on models that impose strong explicit and/or implicit priori assumptions. As a consequence, the clustering effects tend to be unnatural and stray away from the intrinsic grouping natures of a document collection. We apply a novel graph-theoretic technique called Clique Percolation Method (CPM) for document clustering. In this method, a process of enumerating highly cohesive maximal document cliques is performed in a random graph, where those strongly adjacent cliques are mingled to form naturally overlapping clusters. Our clustering results can unveil the inherent structural connections of the underlying data. Experiments show that CPM can outperform some typical algorithms on benchmark data sets, and shed light on its advantages on natural document clustering.