Efficient box-counting determination of generalized fractal dimensions

Abstract

An alternative algorithm for the numerical analysis of fractal structures and measures is presented, which consumes computer time and memory only quasiproportionally to the size of the input data set. This efficient tool is applied to various deterministic and random multifractals, in particular to the growth probability measures of diffusion-limited aggregation clusters in two- and three-dimensional embedding space.