Application and Comparison of Biclustering Methods in Detecting Crime Regions
Nazan SARI Sümeyye Gizem ÇAKAR Olcay EYDEMİR İbrahim ÇİL
AbstractCrime analysis has importance for the detection of crime regions, the prediction of crimes before processing and the security forces to take necessary measures. By using biclustering methods to detect crime regions, simultaneous clustering of the types of crimes and regions where crime is committed to producing more comprehensive results than traditional clustering methods. In this study, CC and Xmotif algorithms of biclustering methods were applied to the real data set in order to detect the crime regions. “Crimes in Boston” data set was used in real data set application. In order to measure the efficiency of the biclusters, the performance of the algorithms was compared with Chia and Karuturi bicluster score (CCPS). The results were obtained by using Matlab functions and it was observed that results of the CC algorithm were better compared to Xmotif algorithm.