Discovery of Spatial Association Rules from Fuzzy Spatial Data

Silva, Henrique P. da; Felix, Thiago D. R.; Venâncio, Pedro Vinícius Almeida Borges de; Carniel, Anderson Chaves

doi:10.1007/978-3-031-17995-2_13

Cited by 3 publications

(3 citation statements)

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“…For instance, we intend to understand the effect of varying the policies in the two‐stage approach when building fuzzy spatial objects from real datasets. The case studies can also provide guidelines on how to integrate the package fsr into existing GIS, SDBS, and SDS projects (e.g., in the context of extracting spatial association rules, as described by da Silva et al (2022)).…”

Section: Discussionmentioning

confidence: 99%

fsr: An R package for fuzzy spatial data handling

Carniel

Venâncio

Schneider

2023

Transactions in GIS

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Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r-project.org/package=fsr. Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations.

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Section: Discussionmentioning

confidence: 99%

fsr: An R package for fuzzy spatial data handling

Carniel

Venâncio

Schneider

2023

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“…Some interesting approaches have focused on spatial data. Spatial association rules can be implemented by permitting spatial application features represented by fuzzy spatial objects and topological relationships [6]. They extract association rules using applicationrelated spatial objects of interest and the fuzzy spatial features.…”

Section: Fuzzy Spatial Association Rulesmentioning

confidence: 99%

“…The use of fuzzy sets is well established in managing real-world applications in which uncertainty is commonly involved. More specifically for our interest here in association rules, a number of approaches have been considered for fuzzy data [5][6][7].While fuzzy sets present some capabilities, a simple membership function has limitations [8]. This motivates our use of intuitionistic fuzzy sets, as they provide more flexibility for human interactions under uncertainty in association rule data mining.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Using Association Rules for Intuitionistic Fuzzy Data

Petry

Yager

2023

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This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association rules can provide guidance for assessing the significant relationships that can be determined while analyzing data. The approach uses the cardinality of intuitionistic fuzzy sets that provide a minimum and maximum range for the support and confidence metrics. A new notation is used to enable the representation of the fuzzy metrics. A running example of queries about the desirable features of vacation locations is used to illustrate.

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