Chinese Academy of Sciences

Advanced satellite tracking technologies have collected huge amounts of wild birds ’ migration data. These data are very useful for biologists to understand birds ’ dynamic mi-gration patterns, to study correlations between the habitats, and to predict global spread trends of avian influenza. We transform the biological problem into a machine learning problem by converting the migratory paths of wild birds in-to graphs. Our first step of H5N1 outbreak prediction is to discover weighted closed cliques from the graphs by our mining algorithm HELEN (short for High-wEight cLosed cliquE miNing), which are then used by our learning al-gorithm HELEN-p to predict potential H5N1 outbreaks at habitats. We show that the prediction is more accurate in comparison with that by the traditional method on a migra-tion data set obtained through a real satellite bird-tracking system. It is also confirmed by our empirical analysis that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques