Spatial Association Rule Mining

Spatial Association Rule Mining is a data mining technique that identifies statistically significant co-occurrence patterns among spatial features or objects within geographic space, distinguished by its incorporation of spatial relationships such as proximity, contiguity, and spatial autocorrelation in the rule discovery process.

84

Publications

5.4K

Citations

223

Authors

119

Institutions

Publications per year

2017–2026

23

Authors

223

  1. SS
    Shashi Shekhar

    University of Minnesota, United States

    4 publications · 370 citations

    4

    h-index

  2. KK
    Krzysztof Koperski

    Simon Fraser University, Canada

    4 publications · 330 citations

    4

    h-index

  3. DM
    Donato Malerba

    University of Bari Aldo Moro, Italy

    3 publications · 143 citations

    3

    h-index

  4. HX
    Hui Xiong

    Rutgers, The State University of New Jersey, United States

    3 publications · 173 citations

    3

    h-index

  5. AA
    Annalisa Appice

    University of Bari Aldo Moro, Italy

    3 publications · 143 citations

    3

    h-index

1–5 of 223

Venues

  1. Transactions in GIS

    3 publications · 205 citations

    3

    h-index

  2. Expert Systems with Applications

    2 publications · 110 citations

    2

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. Information Sciences

    2 publications · 152 citations

    2

    h-index

1–5