2009/05/26 by Christos Attikos, Attikos, Christos, Michael Doumpos +1
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Algorithms and Data Compression #Box counting #Combinatorics #Computer science #Correlation dimension #Data Structures and Algorithms (cs.DS) #Data mining #Databases (cs.DB) #Dimension (graph theory) #Effective dimension #FOS: Computer and information sciences #Fractal #Fractal analysis #Fractal dimension #Hausdorff dimension #Mathematical analysis #Mathematics #Measure (data warehouse) #Skewness #Statistics #Theoretical and Computational Physics #Topological and Geometric Data Analysis #cs.DB #cs.DS
paper · pdf · doi:10.48550/arxiv.0905.4138
published in arXiv (Cornell University) (Cornell University) · 4 pages, to appear in BCI 2009 - 4th Balkan Conference in Informatics
arxiv created 2009/05/26 · openalex publication_date 2009/05/26 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.