In this paper we apply graph theory techniques on real data visitors’ paths recorded during an exhibition to detect clusters of stands. We consider in particular the dominant set clustering technique, which finds complete heavy subgraphs in weighted undirected graphs. The resulting overlapping clusters could be used to set a travel recommendation system, identify market segments and assess stand assignment effectiveness.

Graph-based clustering of visitors' trajectories at exhibitions

GENTILIN, MARTINA
Membro del Collaboration Group
;
Pietro Lovato
Membro del Collaboration Group
;
Gloria Menegaz
Membro del Collaboration Group
;
Marco Cristani
Membro del Collaboration Group
;
Marco Minozzo
Membro del Collaboration Group
2019-01-01

Abstract

In this paper we apply graph theory techniques on real data visitors’ paths recorded during an exhibition to detect clusters of stands. We consider in particular the dominant set clustering technique, which finds complete heavy subgraphs in weighted undirected graphs. The resulting overlapping clusters could be used to set a travel recommendation system, identify market segments and assess stand assignment effectiveness.
2019
Clustering, Dominant set, Graph theory, Fuzzy method, Trajectory
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1000830
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