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Fuzzy Set Theory-Based Approach for Mining Spatial Association Rules: Road Accident as a Case Study

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AI and IoT for Sustainable Development in Emerging Countries

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 105))

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Abstract

Despite intensive research over the last decades in spatial data mining, a considerable amount of collected data brings new challenges to the extraction of spatial association rules. Notwithstanding, association rules are examining the numerical and categorical data accurately; however, it is not directly extensible to efficiently analyze real-world spatial data. Analyzing spatial data exposes significant difficulties, including particular representations of spatial information like geometrical measures, topological measures, and spatial object dependencies. Most current studies ignore the integration of geographical information in the association rules mining process, which is not beneficial for the decision-makers to extract relevant association rules. To deal with this issue, we proposed a fuzzy set theory-based approach for mining spatial association rules; This proposed approach would be especially valuable for decision-makers suffering from imprecision and non-quality of extracted association rules. An analysis of road accidents using spatial data shows that the use of fuzzy set theory improves the quality and the precision of extracted association rules. Overall, the proposed approach contributes to a better understanding of spatial association rules and provides meaningful information that can aid decision-makers to improve the performances and precision of spatial association rules.

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Data Availability

The data used to support the findings of this study were inferred from reports provided by the ministry of equipment and transportation.

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Acknowledgements

The authors would like to thank Lili Jiang for her support. Furthermore, we are grateful for ISCL and Uppsala University in Sweden.

Author information

Authors and Affiliations

  1. Department of Computing Science, Uppsala University, Uppsala, Sweden

    Addi Ait-Mlouk

  2. Department of Geology, Cadi Ayyad University, Marrakech, Morocco

    Mohamed Ait-Mlouk

  3. IETR laboratory UMR 6164, Polytech de Nantes university, Nantes, France

    Fatima-Zahra El Mazouri

  4. Department of Computing Science, Saroj Mohan Institute of Technology, Kolkata, India

    Arindam Dey

  5. Department of Computing Science, Cadi Ayyad University, Marrakech, Morocco

    Tarik Agouti

Authors
  1. Addi Ait-Mlouk
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  2. Mohamed Ait-Mlouk
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  3. Fatima-Zahra El Mazouri
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  4. Arindam Dey
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  5. Tarik Agouti
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Corresponding author

Correspondence to Addi Ait-Mlouk .

Editor information

Editors and Affiliations

  1. LIM, Hassan II University, Mohammedia, Morocco

    Zakaria Boulouard

  2. Moulay Ismail University, Meknes, Morocco

    Mariya Ouaissa

  3. Moulay Ismail University, Meknes, Morocco

    Mariyam Ouaissa

  4. Sidi Mohamed Ben Abdellah University, Fes, Morocco

    Sarah El Himer

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Ait-Mlouk, A., Ait-Mlouk, M., El Mazouri, FZ., Dey, A., Agouti, T. (2022). Fuzzy Set Theory-Based Approach for Mining Spatial Association Rules: Road Accident as a Case Study. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_17

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