The algorithm to determine A is based on that of Henkelman et. al. , but applied to a graph rather than to a uniform grid. A schematic is shown in Fig. and the steps are detailed below: As noted in , step 6 of this algorithm allows rediscovery of previous steepest ascent paths and significantly improves runtime performance. Once we have constructed the map A : G → M L (G) associating points to local maxima, we turn our attention to the algorithm to cluster local maxima into families FIG. . Regions with a deviation d(xi, x) of less than t = 0.25 for two maxima x1 and x2 of a function f (x). Note that the region for the smaller maxima is smaller, thanks to the scaleindependence of the deviation measure (Eq. 16).