A Hypergraph-Based Model for Graph Clustering: Application to Image Indexing

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.

• Type: Conference
• Year: 2009
• Where: CAIP
• Authors: Salim Jouili, Salvatore Tabbone
• Keywords: CAIP 2009, Graph Data Sets, Graph Median, Pattern Recognition, Prototype-based Clustering Algorithm