Data Cleaning

Data Cleaning is a data preprocessing activity within the broader category of data management and analysis processes, distinguished by its focus on detecting, correcting, or removing inaccurate, incomplete, irrelevant, or duplicated data entries to ensure data quality and consistency for subsequent analytics, mining, engineering, or clinical and spatial data applications.

1.2K

Publications

97.2K

Citations

3.2K

Authors

1.2K

Institutions

Publications per year

2017–2026

401

Authors

3.2K

  1. NT
    Nan Tang

    Tsinghua University, China

    26 publications · 2.2K citations

    21

    h-index

  2. IF
    Ihab F. Ilyas

    University of Waterloo, Canada

    20 publications · 2.8K citations

    20

    h-index

  3. MO
    Mourad Ouzzani

    Purdue University West Lafayette, United States

    19 publications · 1.7K citations

    17

    h-index

  4. PP
    Paolo Papotti

    EURECOM, France

    17 publications · 1.7K citations

    14

    h-index

  5. FG
    Floris Geerts

    University of Edinburgh, United Kingdom

    14 publications · 1.3K citations

    14

    h-index

1–5 of 3.2K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 40 publications · 3.2K citations

    31

    h-index

  2. Information Sciences

    18 publications · 1K citations

    17

    h-index

  3. arXiv (Cornell University)

    Repository · 15 publications · 483 citations

    13

    h-index

  4. Expert Systems with Applications

    13 publications · 455 citations

    13

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 10 publications · 1.2K citations

    10

    h-index

1–5