Faster Estimation of the Correlation Fractal Dimension Using Box-counting
Faster Estimation of the Correlation Fractal Dimension Using Box-counting
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
- Technical University of Crete Greece
- Accenture (Switzerland) Switzerland
- University of Crete Greece
FOS: Computer and information sciences, Computer Science - Databases, Computer Science - Data Structures and Algorithms, Databases (cs.DB), Data Structures and Algorithms (cs.DS)
FOS: Computer and information sciences, Computer Science - Databases, Computer Science - Data Structures and Algorithms, Databases (cs.DB), Data Structures and Algorithms (cs.DS)
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