Advanced satellite tracking technologies have collected huge amounts of wild birds&#x2019; migration data. These data are very useful for biologists to understand birds&#x2019; dynamic migration 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 into 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 algorithm 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 migration 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.