WIT Press


Spectral Clustering And Community Detection In Document Networks

Price

Free (open access)

Paper DOI

10.2495/DATA090051

Volume

42

Pages

10

Page Range

41 - 50

Published

2009

Size

368 kb

Author(s)

C. K. dos Santos, A. G. Evsukoff & B. S. L. P. de Lima

Abstract

Document clustering is one of the most active research topics in text mining. In this work two approaches issued from very different fields are explored for document clustering: spectral clustering and community detection in complex networks. Both approaches are based on a representation of the document collection as a graph, of which the nodes represent the documents and the edges represent the similarities between each pair of documents, such that the two approaches have many issues in common. The results of the application of these two types of techniques to benchmark text mining problems show that they are complementary and are useful for finding structure in large collections of documents Keywords: text mining, document clustering, spectral clustering, community detection, complex networks, modularity.

Keywords

text mining, document clustering, spectral clustering, community detection, complex networks, modularity.