Hypergraph Modelling and Graph Clustering Process Applied to Co-word Analysis
Résumé
We argue that any document set can be modelled as a hypergraph, and we apply a graph clustering process as a way of analysis. A variant of the single link clustering is presented, and we assert that it is better suited to extract interesting clusters formed along easily interpretable paths of associated items than algorithms based on detecting high density regions. We propose a methodology that involves the extraction of similarity graphs from the indexed-dataset represented as a hypergraph. The mining of informative short paths or geodesics in the graphs follows a graph reduction process. An application for testing this methodology is briefly exposed. We close this paper indicating the future work.
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