Similarity Detection

Similarity Detection is a data reduction technique that identifies near-duplicate data segments by measuring content resemblance, distinguishing it from exact matching through its reliance on approximate similarity metrics.

1.4K

Publications

74.7K

Citations

3.8K

Authors

1.2K

Institutions

Publications per year

2017–2026

302

Variants

Near-duplicate Detection

Authors

3.8K

  1. FN
    Felix Naumann

    Hasso Plattner Institute, Germany

    17 publications · 1K citations

    14

    h-index

  2. XZ
    Xiaofang Zhou

    The University of Queensland, Australia

    11 publications · 275 citations

    10

    h-index

  3. WW
    Wei Wang

    University of California, Los Angeles, United States

    10 publications · 1.8K citations

    10

    h-index

  4. HT
    Heng Tao Shen

    University of Electronic Science and Technology of China, China

    8 publications · 215 citations

    8

    h-index

  5. CX
    Chuan Xiao

    Nagoya University, Japan

    8 publications · 1.1K citations

    8

    h-index

1–5 of 3.8K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 29 publications · 2K citations

    21

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 19 publications · 1.3K citations

    16

    h-index

  3. Expert Systems with Applications

    11 publications · 411 citations

    11

    h-index

  4. Digital Investigation

    11 publications · 475 citations

    11

    h-index

  5. Multimedia Tools and Applications

    13 publications · 303 citations

    11

    h-index

1–5