A data mining approach to identify perpetrators: an integration framework and case studies

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dc.contributorParrish, Allen Scott
dc.contributorSmith, Randy K.
dc.contributorHong, Xiaoyan
dc.contributorForde, David R.
dc.contributor.advisorDixon, Brandon
dc.contributor.authorDing, Li
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-03-01T14:36:25Z
dc.date.available2017-03-01T14:36:25Z
dc.date.issued2010
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractData mining and social network analysis have been widely used in law enforcement to solve crimes. Research questions such as strength of ties in social networks, crime pattern discovery and prioritizing offenders have been studied in this area. However, most of those studies failed to consider the noisy nature of the data. The techniques they proposed only have been applied to small scale data sets. Therefore, it is an important task to design a framework that can work on large scale data sets and tolerance noisy data. In this dissertation, we built an integrated crime detection framework that combined two data mining techniques: decision tree and genetic algorithm and graph theories to solve the problems we pointed out. Our crime pattern analysis is based on all offenders of the state of Alabama in the past 50 years. Our constructed social network contains all Alabama residents. It allows us to fully evaluate the proposed models. Two case studies have been conducted to evaluate the framework. One is based on 625 inmates released from Madison county jail in 2004. Our experimental results show that our recommended risk level has strong correlation in predicting future offense. Another case study is based on the 100 real police reports. The experimental results show that the median ranking of arrestees remains at the top 3% of the return list.en_US
dc.format.extent94 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0000425
dc.identifier.otherDing_alatus_0004D_10430
dc.identifier.urihttps://ir.ua.edu/handle/123456789/930
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectComputer science
dc.titleA data mining approach to identify perpetrators: an integration framework and case studiesen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Computer Science
etdms.degree.disciplineComputer Science
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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