Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis

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dc.contributor.authorFarias, Adriana M. Guimaraes de
dc.contributor.authorCintra, Marcos E. [UNIFESP]
dc.contributor.authorFelix, Angelica C.
dc.contributor.authorCavalcante, Danniel L.
dc.date.accessioned2018-07-26T12:18:33Z
dc.date.available2018-07-26T12:18:33Z
dc.date.issued2018
dc.description.abstractPublic security has always been an important research topic. In this sense, machine learning algorithms have been used to extract knowledge from criminal databases, which usually maintain records in order to generate statistics. The automatic extraction of knowledge from such databases allows the improvement and planning of strategies to prevent and combat crimes. Accordingly, in this work different models related to public security are presented. Such models are based on clustering algorithms, on the analysis of formal concept techniques, and on the analysis of crime record data collected in the city of Mossoro, Brazil. The two types of models generated are: (i) concept lattices with crime patternsen
dc.description.abstract(ii) criminal hot spot maps. We also produced a ranking of dangerousness for neighbourhoods of Mossoro. The Fuzzy K-Means clustering algorithm was used to obtain criminal hot spots, which indicate locations with high crime incidence. Formal concept analysis was used for extracting visual models describing patterns that characterize criminal activities. Such models have the form of conceptual lattices that provide graphical displays which can be used for defining strategies to combat and prevent crime. The models were first empirically evaluated and then analysed by public security experts, who provided positive feedback for their practical use. The advantages of the automatically generated models presented in this paper are many, including the short time to produce such models, the variety of different models that can be generated for specific regions and periods of days, months, or years, the graphical characteristic of such models that allow a fast analysis of them, as well as the use of large amounts of data, which are infeasible activities to be done by human experts.en
dc.description.affiliationFed Rural Univ Semi Arid, Ctr Exact & Nat Sci, Mossoro, RN, Brazil
dc.description.affiliationUniv Fed Sao Paulo, Inst Sci & Technol, Ave Cesare Mansueto Giulio Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUnifespUniv Fed Sao Paulo, Inst Sci & Technol, Ave Cesare Mansueto Giulio Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipCoordination for the Improvement of Higher Education Personnel (CAPES)
dc.description.sponsorshipBrazilian National Council for Scientific and Technological Development (CNPq)
dc.format.extent429-452
dc.identifierhttp://dx.doi.org/10.1142/S0218488518500216
dc.identifier.citationInternational Journal Of Uncertainty Fuzziness And Knowledge-Based Systems. Singapore, v. 26, n. 3, p. 429-452, 2018.
dc.identifier.doi10.1142/S0218488518500216
dc.identifier.issn0218-4885
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/45982
dc.identifier.wosWOS:000433949300004
dc.language.isoeng
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectPublic securityen
dc.subjectcrime preventionen
dc.subjectcriminal hot spotsen
dc.subjectfuzzy clusteringen
dc.subjectclustering algorithmsen
dc.subjectformal concept analysisen
dc.titleDefinition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysisen
dc.typeinfo:eu-repo/semantics/article
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