Crime information improvement for situation awareness based on data mining
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-16657-1_75 http://hdl.handle.net/11449/221303 |
Resumo: | Crime records hold critical information about crime situations including the offender actions, stolen objects description, characteristics of victims, the location of the crime situation, individuals involved and more. To consume crime data, police forces and other security analysts use risk management systems which process and organize data, and summarize it into relevant and useful information on criminal situations. This type of system depends on promoting Situation Awareness to stimulate the users understanding of the crime situations and consequently the decision-making assertiveness. In this work the goal is to contribute with the typification of crime situations by machine learning driven techniques, applied in conjunction with pre-processing and transformation. Results showed that the use of pre-processing techniques improved data quality and algorithms precision. In addition, the transformation technique with the best results found was Bag of Words Binarization. Finally, the Logistic Regression algorithm presented the best results for mining the crime data. |
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Crime information improvement for situation awareness based on data miningCrime dataData miningKnowledge discoverySituation awarenessCrime records hold critical information about crime situations including the offender actions, stolen objects description, characteristics of victims, the location of the crime situation, individuals involved and more. To consume crime data, police forces and other security analysts use risk management systems which process and organize data, and summarize it into relevant and useful information on criminal situations. This type of system depends on promoting Situation Awareness to stimulate the users understanding of the crime situations and consequently the decision-making assertiveness. In this work the goal is to contribute with the typification of crime situations by machine learning driven techniques, applied in conjunction with pre-processing and transformation. Results showed that the use of pre-processing techniques improved data quality and algorithms precision. In addition, the transformation technique with the best results found was Bag of Words Binarization. Finally, the Logistic Regression algorithm presented the best results for mining the crime data.Computer Networks Lab State University of Campinas, Cidade UniversitáriaHuman-Computer Interaction Group University Centre Euripides of Marília, 529 Hygino Muzzi Filho AvenueSão Paulo State University, 737 Hygino Muzzi Filho AvenueSão Paulo State University, 737 Hygino Muzzi Filho AvenueUniversidade Estadual de Campinas (UNICAMP)University Centre Euripides of MaríliaUniversidade Estadual Paulista (UNESP)Ladeira, Lucas ZancoJunior, Valdir Amancio Pereira [UNESP]Rodrigues, Raphael ZanonBotega, Leonardo Castro [UNESP]2022-04-28T19:27:22Z2022-04-28T19:27:22Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject803-812http://dx.doi.org/10.1007/978-3-030-16657-1_75Advances in Intelligent Systems and Computing, v. 940, p. 803-812.2194-53652194-5357http://hdl.handle.net/11449/22130310.1007/978-3-030-16657-1_752-s2.0-85066320723Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAdvances in Intelligent Systems and Computinginfo:eu-repo/semantics/openAccess2022-04-28T19:27:22Zoai:repositorio.unesp.br:11449/221303Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:27:22Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Crime information improvement for situation awareness based on data mining |
title |
Crime information improvement for situation awareness based on data mining |
spellingShingle |
Crime information improvement for situation awareness based on data mining Ladeira, Lucas Zanco Crime data Data mining Knowledge discovery Situation awareness |
title_short |
Crime information improvement for situation awareness based on data mining |
title_full |
Crime information improvement for situation awareness based on data mining |
title_fullStr |
Crime information improvement for situation awareness based on data mining |
title_full_unstemmed |
Crime information improvement for situation awareness based on data mining |
title_sort |
Crime information improvement for situation awareness based on data mining |
author |
Ladeira, Lucas Zanco |
author_facet |
Ladeira, Lucas Zanco Junior, Valdir Amancio Pereira [UNESP] Rodrigues, Raphael Zanon Botega, Leonardo Castro [UNESP] |
author_role |
author |
author2 |
Junior, Valdir Amancio Pereira [UNESP] Rodrigues, Raphael Zanon Botega, Leonardo Castro [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) University Centre Euripides of Marília Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Ladeira, Lucas Zanco Junior, Valdir Amancio Pereira [UNESP] Rodrigues, Raphael Zanon Botega, Leonardo Castro [UNESP] |
dc.subject.por.fl_str_mv |
Crime data Data mining Knowledge discovery Situation awareness |
topic |
Crime data Data mining Knowledge discovery Situation awareness |
description |
Crime records hold critical information about crime situations including the offender actions, stolen objects description, characteristics of victims, the location of the crime situation, individuals involved and more. To consume crime data, police forces and other security analysts use risk management systems which process and organize data, and summarize it into relevant and useful information on criminal situations. This type of system depends on promoting Situation Awareness to stimulate the users understanding of the crime situations and consequently the decision-making assertiveness. In this work the goal is to contribute with the typification of crime situations by machine learning driven techniques, applied in conjunction with pre-processing and transformation. Results showed that the use of pre-processing techniques improved data quality and algorithms precision. In addition, the transformation technique with the best results found was Bag of Words Binarization. Finally, the Logistic Regression algorithm presented the best results for mining the crime data. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2022-04-28T19:27:22Z 2022-04-28T19:27:22Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-16657-1_75 Advances in Intelligent Systems and Computing, v. 940, p. 803-812. 2194-5365 2194-5357 http://hdl.handle.net/11449/221303 10.1007/978-3-030-16657-1_75 2-s2.0-85066320723 |
url |
http://dx.doi.org/10.1007/978-3-030-16657-1_75 http://hdl.handle.net/11449/221303 |
identifier_str_mv |
Advances in Intelligent Systems and Computing, v. 940, p. 803-812. 2194-5365 2194-5357 10.1007/978-3-030-16657-1_75 2-s2.0-85066320723 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Advances in Intelligent Systems and Computing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
803-812 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965357721518080 |