Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2006

Abstract

Document clustering techniques mostly depend on models that impose explicit and/or implicit priori assumptions as to the number, size, disjunction characteristics of clusters, and/or the probability distribution of clustered data. As a result, the clustering effects tend to be unnatural and stray away more or less from the intrinsic grouping nature among the documents in a corpus. We propose a novel graph-theoretic technique called Clique Percolation Clustering (CPC). It models clustering as a process of enumerating adjacent maximal cliques in a random graph that unveils inherent structure of the underlying data, in which we unleash the commonly practiced constraints in order to discover natural overlapping clusters. Experiments show that CPC can outperform some typical algorithms on benchmark data sets, and shed light on natural document clustering.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 3rd Asia Information Retrieval Symposium (AIRS 2006)

First Page

119

Last Page

131

Identifier

10.1007/11880592_10

Publisher

Springer

City or Country

Singapore

Additional URL

https://doi.org/10.1007/11880592_10

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