Incremental Filtering

Incremental Filtering is a dynamic filtering methodology that progressively refines results by incorporating newly arriving data in a sequential manner, distinguishing itself from batch or static filtering approaches through its iterative update mechanism.

31

Publications

1.2K

Citations

96

Authors

60

Institutions

Publications per year

2017–2026

4

Authors

96

  1. KK
    Keshab K. Parhi

    University of Minnesota, United States

    2 publications · 179 citations

    2

    h-index

  2. ML
    Min Li

    Jiangxi University of Water Resources and Electric Power, China

    2 publications · 49 citations

    2

    h-index

  3. IS
    Ian Soboroff

    National Institute of Standards and Technology, United States

    2 publications · 33 citations

    2

    h-index

  4. MK
    Max Kleiman‐Weiner

    Massachusetts Institute of Technology, United States

    2 publications · 33 citations

    2

    h-index

  5. YX
    Yunni Xia

    Chongqing University, China

    2 publications · 251 citations

    2

    h-index

1–5 of 96

Venues

  1. Monthly Weather Review

    2 publications · 110 citations

    2

    h-index

  2. Knowledge-Based Systems

    2 publications · 251 citations

    2

    h-index

  3. Text REtrieval Conference

    Conference · 2 publications · 33 citations

    2

    h-index

  4. IEEE Transactions on Image Processing

    1 publications · 18 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 55 citations

    1

    h-index

1–5