Missing Data Identification

Missing Data Identification is a systematic method within the field of data extraction that distinguishes itself by detecting and characterizing the absence of expected data elements in a dataset, thereby enabling subsequent handling of incomplete information.

293

Publications

38.7K

Citations

748

Authors

404

Institutions

Publications per year

2017–2026

77

Authors

748

  1. FV
    Fulufhelo V. Nelwamondo

    University of Johannesburg, South Africa

    5 publications · 73 citations

    5

    h-index

  2. TM
    Tshilidzi Marwala

    University of the Witwatersrand, South Africa

    4 publications · 59 citations

    4

    h-index

  3. JW
    Jerzy W. Grzymala‐Busse

    University of Kansas, United States

    3 publications · 60 citations

    3

    h-index

  4. CK
    Craig K. Enders

    Arizona State University, United States

    3 publications · 981 citations

    3

    h-index

  5. AI
    Alf Isaksson

    KTH Royal Institute of Technology, Sweden

    3 publications · 170 citations

    3

    h-index

1–5 of 748

Venues

  1. 8

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 7 publications · 321 citations

    7

    h-index

  3. Knowledge-Based Systems

    6 publications · 652 citations

    6

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 138 citations

    5

    h-index

  5. Psychological Methods

    5 publications · 13K citations

    5

    h-index

1–5