  Fractal dimension is widely adopted in spatial databases and data mining,
among others as a measure of dataset skewness. State-of-the-art algorithms for
estimating the fractal dimension exhibit linear runtime complexity whether
based on box-counting or approximation schemes. In this paper, we revisit a
correlation fractal dimension estimation algorithm that redundantly rescans the
dataset and, extending that work, we propose another linear, yet faster and as
accurate method, which completes in a single pass.
