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Journal of Artificial Intelligence
  Year: 2015 | Volume: 8 | Issue: 1 | Page No.: 17-34
DOI: 10.3923/jai.2015.17.34
 
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A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

Noor Maizura Mohamad Noor, Siti Haslini Ab Hamid, Rosmayati Mohemad, Masita @ Masila Abdul Jalil and Muhammad Suzuri Hitam

Abstract:
Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.
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How to cite this article:

Noor Maizura Mohamad Noor, Siti Haslini Ab Hamid, Rosmayati Mohemad, Masita @ Masila Abdul Jalil and Muhammad Suzuri Hitam, 2015. A Review on a Classification Framework for Supporting Decision Making in Crime Prevention. Journal of Artificial Intelligence, 8: 17-34.

DOI: 10.3923/jai.2015.17.34

URL: https://scialert.net/abstract/?doi=jai.2015.17.34

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