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A hypergraph-based model for graph clustering: application to image indexing

Salim Jouili 1 Salvatore Tabbone 1 
1 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we introduce a prototype-based clustering algorithm dealing with graphs. We propose a hypergraph-based model for graph data sets by allowing clusters overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we define a retrieval technique indexing the database with hyperedge centroids. This model is interesting to travel the data set and efficient to cluster and retrieve graphs.
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Submitted on : Thursday, July 16, 2009 - 11:20:29 AM
Last modification on : Friday, February 4, 2022 - 3:12:03 AM
Long-term archiving on: : Thursday, June 30, 2011 - 11:44:17 AM


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Salim Jouili, Salvatore Tabbone. A hypergraph-based model for graph clustering: application to image indexing. The 13th International Conference on Computer Analysis of Images and Patterns, Sep 2009, Munster, Germany. pp.360-368, ⟨10.1007/978-3-642-03767-2_44⟩. ⟨inria-00404323⟩



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