On building and visualizing proximity graphs for large data sets with artificial ants

Abstract

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. © Springer-Verlag Berlin Heidelberg 2011.