Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Data association methods with applications to law enforcement
Decision Support Systems
TimeSleuth: A Tool for Discovering Causal and Temporal Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Discovering Trends in Large Datasets Using Neural Networks
Applied Intelligence
Efficient rule discovery in a geo-spatial decision support system
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Enhancing border security: Mutual information analysis to identify suspect vehicles
Decision Support Systems
Use of data mining techniques to model crime scene investigator performance
Knowledge-Based Systems
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Mining term networks from text collections for crime investigation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In the recent era of increasing volume crimes, crime prevention is now one of the most important global issues, along with the great concern of strengthening public security. Government and community officials are making an all-out effort to improve the effectiveness of crime prevention. Numerous investigations addressing this problem have generally employed disciplines of behavior science and statistics. Recently, the data mining approach has been shown to be a proactive decision-support tool in predicting and preventing crime. However its effectiveness is often limited due to different natures of crime data, such as linguistic crime data evolving over time. In this paper, we propose a framework of intelligent decision-support model based on a fuzzy self-organizing map (FSOM) network to detect and analyze crime trend patterns from temporal crime activity data. In addition, a rule extraction algorithm is employed to uncover hidden causal-effect knowledge and reveal the shift around effect. In contrast to most present crime related studies, we target a non-Western real-world case, i.e. the National Police Agency (NPA) in Taiwan. The resultant model can support police managers in assessing more appropriate law enforcement strategies, as well as improving the use of police duty deployment for crime prevention.