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[2P5-GS-3-05] Characteristic Evaluation of Edge-Adding-Based Hypergraph Clustering Methods
Keywords:Hypergraph, clustering
In recent years, studies on hypergraph that is a generalization of graphs and can represent relationships of two or more nodes, have been actively conducted, however, clustering methods over them have not been established yet.
In this study, we propose a fast clustering method where a hypergraph is expanded to a bipartite graph by treating hyperedges as nodes, the relationship between the node and the hyperedge is defined by TF-IDF, and the value is treated as the weight of the bipartite-graph-edge.
Our algorithm can efficiently grasp clusters by reconstructing bipartite graph edges in descending order of TF-IDF weights and merging nodes reachable along the edges into clusters.
Experimental evaluations using artificial data and large-scale real datasets show that our method is superior in terms of effectiveness and efficiency compared to an existing method.
In this study, we propose a fast clustering method where a hypergraph is expanded to a bipartite graph by treating hyperedges as nodes, the relationship between the node and the hyperedge is defined by TF-IDF, and the value is treated as the weight of the bipartite-graph-edge.
Our algorithm can efficiently grasp clusters by reconstructing bipartite graph edges in descending order of TF-IDF weights and merging nodes reachable along the edges into clusters.
Experimental evaluations using artificial data and large-scale real datasets show that our method is superior in terms of effectiveness and efficiency compared to an existing method.
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