A SURVEY OF BIG DATA AND DATA MINING TECHNIQUES FOR CRIME PREVENTION

Crimes remain a serious challenge to many societies and nations across the globe, despite technological advancement. Efforts by security agencies must remain a step ahead of potential attacks in order to effectively prevent crimes from occurring. The police stations and other related criminal justic...

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Bibliographic Details
Published inCompusoft: an international journal of advanced computer technology Vol. 9; no. 11; pp. 3927 - 3933
Main Authors Noor, Ahmad Shukri Mohd, Sanusi, Saadatu Suleiman, Rahim, Noor Hafhizah Abd
Format Journal Article
LanguageEnglish
Published Mumbai COMPUSOFT, An International Journal of Advanced Computer Technology 01.11.2020
Subjects
Algorithms
Big Data
Classification
Cluster analysis
Crime
Crime prevention
Criminal investigations
Data analysis
Data mining
Decision trees
Intelligence services
Network analysis
Police
Police departments
Social network analysis
Social networks
Nigeria
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Summary:Crimes remain a serious challenge to many societies and nations across the globe, despite technological advancement. Efforts by security agencies must remain a step ahead of potential attacks in order to effectively prevent crimes from occurring. The police stations and other related criminal justice departments have several large databases that can be used to forecast or examine criminal activities. Data Mining, the method of uncovering sensitive information from big data, is now an effective tool for combating, curbing and preventing crimes of all sorts. The main aim of this paper is to provide a systematic survey of how Data Mining techniques are applicable in combating crimes. In this paper, we selected widely applicable data mining techniques that were commonly used for crime analysis and prevention in previous related studies. A summary table of many relevant crime- Data Mining applications is included that will serve as a reference for researchers. Each technique has its specific use. Data mining methods are quickly explained to the reader that includes Classification, Cluster analysis, Association rue mining, Entity extraction and Social network analysis amongst others.
ISSN:2320-0790