Analytical Model of Recommendations for the Mitigation of Theft Risks

Juan Camilo Montaña, Enrique Gonzalez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Crime rates around the world are constantly increasing, and the crime of theft is one of those that most affects the population. This type of crime has occurrences and patterns in certain places and periods. This article presents an analytical model that allows generating recommendations to mitigate the risk of being a victim of this crime. The model is responsible for preprocessing the data, performing fuzzy partitioning, generating frequent patterns, and creating fuzzy association rules to generate recommendations. The model was applied to the case study associated with the area with the highest theft crimes rate in Bogotá.

Original languageEnglish
Title of host publicationAdvances in Computing - 15th Colombian Congress, CCC 2021, Revised Selected Papers
EditorsEnrique Gonzalez, Mariela Curiel, Andrés Moreno, Angela Carrillo-Ramos, Rafael Páez, Leonardo Flórez-Valencia
PublisherSpringer Science and Business Media Deutschland GmbH
Pages31-45
Number of pages15
ISBN (Print)9783031199509
DOIs
StatePublished - 2022
Event15th Colombian Congress on Advances in Computing, CCC 2021 - Bogota, Colombia
Duration: 22 Nov 202126 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1594 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference15th Colombian Congress on Advances in Computing, CCC 2021
Country/TerritoryColombia
CityBogota
Period22/11/2126/11/21

Keywords

  • Association rules
  • Fuzzy partitioning
  • Recommendations model
  • Theft risk

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