Faster Estimation of the Correlation Fractal Dimension Using Box-counting

Author

Attikos, Christos ; Doumpos, Michael

Author_Institution

Manage. Consulting Div., Accenture S.A., Kifissia, Greece

fYear

2009

fDate

17-19 Sept. 2009

Firstpage

93

Lastpage

95

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.

Keywords

correlation methods; data mining; fractals; visual databases; approximation schemes; box-counting; correlation fractal dimension faster estimation; data mining; dataset skewness; linear runtime complexity; spatial databases; state-of-the-art algorithms; Approximation algorithms; Clustering algorithms; Conference management; Data mining; Electronic mail; Fractals; Informatics; Runtime; Spatial databases; State estimation; Box-counting; Databases; Fractal dimension;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics, 2009. BCI '09. Fourth Balkan Conference in

Conference_Location

Thessaloniki

Print_ISBN

978-0-7695-3783-2

Type

conf

DOI

10.1109/BCI.2009.6

Filename

5359325