High-dimensional Pattern Discovery

High-Dimensional Pattern Discovery is a methodological framework within high-dimensional data analysis that enables the identification and interpretation of latent structural regularities in data characterized by a large number of interrelated variables.

55

Publications

2.2K

Citations

204

Authors

107

Institutions

Publications per year

2017–2026

14

Authors

204

  1. RV
    Renato Vimieiro

    University of Newcastle Australia, Australia

    3 publications · 48 citations

    3

    h-index

  2. PM
    Pablo Moscato

    University of Newcastle Australia, Australia

    2 publications · 27 citations

    2

    h-index

  3. MJ
    M. J. R. Healy

    Experimental Station, United States

    1 publications · 19 citations

    1

    h-index

  4. MS
    Mahito Sugiyama

    The University of Osaka, Japan

    1 publications · 16 citations

    1

    h-index

  5. SB
    Sanjay Bapna

    Morgan State University, United States

    1 publications · 13 citations

    1

    h-index

1–5 of 204

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 97 citations

    3

    h-index

  2. Bioinformatics

    2 publications · 183 citations

    2

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 71 citations

    2

    h-index

  4. Max Planck Digital Library

    Repository · 2 publications · 47 citations

    2

    h-index

  5. Knowledge-Based Systems

    2 publications · 70 citations

    2

    h-index

1–5