On Building and Visualizing Proximity Graphs for Large Data Sets with Artificial Ants

Julien Lavergne (1) , Hanene Azzag (2) , Christiane Guinot (3) , Gilles Venturini (1)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
2 LIPN - Laboratoire d'Informatique de Paris-Nord
3 CERIES - CENTRE DE RECHERCHES ET D'INVESTIGATIONS EPIDERMIQUES ET SENSORIELLES

We present in this paper a new incremental and bio-inspired algorithm that builds proximity graphs for large amounts of data (i.e. 1 million). It is inspired from the self-assembly behavior of real ants where each ant progressively becomes attached to an existing support and then successively to other attached ants. The ants that we have defined will similarly build a complex hierarchical graph structure. Each artificial ant represents one data. The way ants move and connect depends on the similarity between data. Our hierarchical extension, for huge amounts of data, gives encouraging running times compared to other incremental building methods and is particularly well adapted to the visualization of groups of data (i.e. clusters) thanks to the super-node structure. In addition the visualization using a force-directed algorithm respects the real distances between data.

Domaines: Interface homme-machine [cs.HC]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Julien Lavergne, Hanene Azzag, Christiane Guinot, Gilles Venturini. On Building and Visualizing Proximity Graphs for Large Data Sets with Artificial Ants. first joint meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society, Jun 2008, Naples, Italy. pp.349-352. ⟨hal-01024518⟩