Crime Data Mining Case Study

Crime data mining: an overview and case studies

Authors: Hsinchun ChenUniversity of Arizona, Tucson, AZ
Wingyan ChungUniversity of Arizona, Tucson, AZ
Yi QinUniversity of Arizona, Tucson, AZ
Michael ChauUniversity of Arizona, Tucson, AZ
Jennifer Jie XuUniversity of Arizona, Tucson, AZ
Gang WangUniversity of Arizona, Tucson, AZ
Rong ZhengUniversity of Arizona, Tucson, AZ
Homa AtabakhshUniversity of Arizona, Tucson, AZ
2003 Article
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Published in:
· Proceeding
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Pages 1-5

Boston, MA, USA — May 18 - 21, 2003
Digital Government Society of North America ©2003
table of contents

computing in governmentdata miningdesignenterprise computingexperimentationmeasurementperformance

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Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime characteristics. Actually crime analysis includes exploring and detecting crimes and their relationships with criminals. The high volume of crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of crime analysis and finally we will use the clustering results in order to perform crime matching process.

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