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Discovery of Spatial Association Rules from Fuzzy Spatial Data

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Conceptual Modeling (ER 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13607))

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Abstract

The discovery of spatial association rules is a core task in spatial data science projects and focuses on extracting useful and meaningful spatial patterns and relationships from spatial and geometric information. Many spatial phenomena have been modeled and represented by fuzzy spatial objects, which have blurred interiors, uncertain boundaries, and/or inexact locations. In this paper, we introduce a novel method for mining spatial association rules from fuzzy spatial data. By allowing users to represent spatial features of their applications as fuzzy spatial objects and by employing fuzzy topological relationships, our method discovers spatial association patterns between spatial objects of users’ interest (e.g., tourist attractions) and such fuzzy spatial features (e.g., sanitary conditions of restaurants, number of reviews and price of accommodations). Further, this paper presents a case study based on real datasets that shows the applicability of our method.

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Notes

  1. 1.

    Details can be found in the implementation of our running example.

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Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Anderson C. Carniel was supported by Google as a recipient of the 2022 Google Research Scholar program.

Author information

Authors and Affiliations

  1. Federal University of Technology - Paraná, Dois Vizinhos, Brazil

    Henrique P. da Silva

  2. Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil

    Thiago D. R. Felix & Anderson C. Carniel

  3. Grad. Program in Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil

    Pedro V. A. B. de Venâncio

Authors
  1. Henrique P. da Silva
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  2. Thiago D. R. Felix
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  3. Pedro V. A. B. de Venâncio
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  4. Anderson C. Carniel
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Corresponding author

Correspondence to Anderson C. Carniel .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Jolita Ralyté

  2. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  3. IIIT Delhi, New Delhi, India

    Mukesh Mohania

  4. University of Skövde, Skövde, Sweden

    Manfred A. Jeusfeld

  5. International Institute of Information Technology Gachibowli, Hyderabad, India

    Kamalakar Karlapalem

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da Silva, H.P., Felix, T.D.R., de Venâncio, P.V.A.B., Carniel, A.C. (2022). Discovery of Spatial Association Rules from Fuzzy Spatial Data. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds) Conceptual Modeling. ER 2022. Lecture Notes in Computer Science, vol 13607. Springer, Cham. https://doi.org/10.1007/978-3-031-17995-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-17995-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17994-5

  • Online ISBN: 978-3-031-17995-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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