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Text Data Network Analysis Using Graph Approach

Authors
  • Polanco, Xavier
  • San Juan, Eric
Publication Date
Oct 25, 2006
Source
HAL-ENAC
Keywords
Language
English
License
Unknown
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Abstract

In this paper we revisit this main idea of co-word analysis based on the computation of all geodesic paths, and it is considered that the variants of single link clustering (SLC) are better suited to extract interesting clusters formed along easily interpretable paths of associated items than algorithms based on detecting high density regions. Moreover, we propose a methodology that involves the extraction of graphs of similarities from the text-data represented on the form of a hypergraph. The mining of informative short paths in these graphs is based on a three-step graph reduction process, and the analysis of these graphs use the degree and betweenness centralities. We conclude with an application for testing this methodology.

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