Detecting Crime Types Using Classification Algorithms
Cui-cui Sun, Chun-long Yao, Xu Li, Kejun Lee
Journal of Digital Information Management, Vol. 12, No. 5, 2014.
DOI: 
Link: http://dline.info/fpaper/jdim/v12i5/5.pdf
Abstract:
Criminal behaviors can reflect the characteristics of the criminals to a great extent. To predict the crime types according to characteristics of vast amounts of criminals is an important part of criminal
behavior analysis. In order to get high classification accuracy, three typical classification algorithms, including
C4.5 algorithm, Naive Bayesian algorithm and K nearest neighbor (KNN) algorithm, are compared using several popular missing data filling  algorithms respectively based on a real crime dataset with lots of missing data. The
experimental results show that higher classification accuracy can be obtained by combining KNN classification algorithm and GBWKNN missing data filling algorithm which is based on grey relational analysis (GRA) theory.