Clique percolation for finding naturally cohesive and overlapping document clusters

• Main Authors: GAO, Wei, WONG, Kam-Fai, XIA, Yunqing, XU, Ruifeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Databases and Information Systems
• 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
• Institution: Singapore Management University

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.