Data-driven Pattern Generation

Data-Driven Pattern Generation is a generative pattern design method that systematically derives and constructs visual or structural patterns through the direct utilization of empirical data, algorithmic inference, and statistical modeling to differentiate it from rule-based or intuition-driven approaches.

20

Publications

605

Citations

57

Authors

26

Institutions

Publications per year

2017–2026

3

Authors

57

  1. IJ
    In-Yong Jang

    Korea University, South Korea

    1 publications · 80 citations

    1

    h-index

  2. TR
    Thorsten Rodner

    University of Kaiserslautern, Germany

    1 publications · 17 citations

    1

    h-index

  3. AŽ
    Andrej Žgank

    University of Maribor, Slovenia

    1 publications · 23 citations

    1

    h-index

  4. JH
    Jianjun He

    State Key Laboratory of Remote Sensing Science, China

    1 publications · 26 citations

    1

    h-index

  5. DP
    Daryl Pregibon

    AT&T (United States), United States

    1 publications · 42 citations

    1

    h-index

1–5 of 57

Venues

  1. Discrete Applied Mathematics

    1 publications · 80 citations

    1

    h-index

  2. Expert Systems with Applications

    1 publications · 19 citations

    1

    h-index

  3. Annals of Operations Research

    1 publications · 13 citations

    1

    h-index

  4. IEEE Internet Computing

    1 publications · 51 citations

    1

    h-index

  5. ACTA AUTOMATICA SINICA

    1 publications · 26 citations

    1

    h-index

1–5