Faster estimation of the correlation fractal dimension using box-counting
Databases
2009-05-27 v1 Data Structures and Algorithms
Abstract
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.
Cite
@article{arxiv.0905.4138,
title = {Faster estimation of the correlation fractal dimension using box-counting},
author = {Christos Attikos and Michael Doumpos},
journal= {arXiv preprint arXiv:0905.4138},
year = {2009}
}
Comments
4 pages, to appear in BCI 2009 - 4th Balkan Conference in Informatics