Faster estimation of the correlation fractal dimension using box-counting

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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.
4 pages, to appear in BCI 2009 - 4th Balkan Conference in Informatics

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Databases, Data Structures and Algorithms

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http://salesiana.dossiersoluciones.com/handle/123456789/231351

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0905.4138
 http://arxiv.org/abs/0905.4138

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arXiv