Faster Estimation of the Correlation Fractal Dimension Using Box-counting

被引：0

作者：

Attikos, Christos ^{[1
]}

Doumpos, Michael ^{[2
]}

机构：

[1] Accenture SA, Management Consulting Div, 1 Arkadias Str, Kifisia 14564, Greece

[2] Tech Univ Crete, Dept Product Engn & Management, Khania GR-73100, Greece

来源：

PROCEEDINGS OF THE 2009 FOURTH BALKAN CONFERENCE IN INFORMATICS | 2009年

关键词：

Fractal dimension; Box-counting; Databases;

D O I：

10.1109/BCI.2009.6

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：93 / +

页数：2

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