  Beerenwinkel et al.(2007) suggested studying fitness landscapes via regular
subdivisions of convex polytopes. Building on their approach we propose cluster
partitions and cluster filtrations of fitness landscapes as a new mathematical
tool. In this way, we provide a concise combinatorial way of processing metric
information from epistatic interactions. Using existing Drosophila microbiome
data, we demonstrate similarities with and differences to the previous
approach. As one outcome we locate interesting epistatic information where the
previous approach is less conclusive.
