Discovery of Association Rules in National Violent Death Data Using Optimization of Number of Attributes
Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.721
Over the last several years, data mining has been successfully used in various areas such as finance, security, telecommunication, science, retail industry, marketing, and Web. In this paper, we explain data mining application to crime analysis. Particularly, this work explains a set of procedures to find optimized number of attributes of the National Violent Death Reporting Database to predict types of violent death that has occurred. We also describe a set of activities of data mining we employed, including data preprocessing, data cleaning, data integration, data discretization, entropy, and information gain. As a result, we have successfully discovered interesting association rules that could be used by law enforcement and government agencies to help prevent violent deaths.
Data mining, entropy, information gain, data attributes, association rules
Seung-Hyun Kim, Craig Dunham, Suryo Muljono, Albert Lee, Taehyung Wang, "Discovery of Association Rules in National Violent Death Data Using Optimization of Number of Attributes", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 616-621, doi:10.1109/CSIE.2009.721