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.

A B S T R A C TRecently, crime rate in Malaysia is increasing from day to day. If this situation is not monitored and there is no drastic action taken, it may cause many serious problems. Therefore, crime prevention is one of the important components of an overall strategy to reduce crime and to strengthen public security. However, the main challenge faced by all law-enforcement and intelligence-gathering organizations is to analyze the growing volumes of crime data accurately and efficiently in order to make decision for crime prevention. Due to difficulty in decision making for crime prevention, Decision Support System (DSS) and data mining approach can be used to resolve the problem. Now-a-days, data mining approach has been exposed to be a practical decision-support concept in predicting and preventing crime. As a result, we propose architecture of DSS using visualization technique because it can represent crime data into more comprehensible presentation. The results from this proposed architecture can support the security authority in assessing more suitable law enforcement strategies, increasing the accuracy of selection decision making, as well as improving the use of security authority duty deployment for crime prevention.