期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：7
DOI：10.14569/IJACSA.2016.070753
出版社：Science and Information Society (SAI)
摘要：In the last five years, crime and accidents rates have increased in many cities of America. The advancement of new technologies can also lead to criminal misuse. In order to reduce incidents, there is a need to understand and examine emerging patterns of criminal activities. This paper analyzed crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime. The dataset is analyzed by using a number of Classification Algorithms. The aim of this study is to highlight trends of incidents that will in return help security agencies and police department to discover precautionary measures from prediction rates. The classification of algorithms used in this study is to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table. The output that has been used in this study, are correct classification, incorrect classification, True Positive Rate (TP), False Positive Rate (FP), Precision (P), Recall (R) and F-measure (F). These outputs are captured by using two different test methods: k-fold cross-validation and percentage split. Outputs are then compared to understand the classifier performances. Our analysis illustrates that JRip has classified the highest number of correct classifications by 73.71% followed by decision table with 73.66% of correct predictions, whereas OneR produced the least number of correct predictions with 64.95%. NaiveBayes took the least time of 0.57 sec to build the model and perform classification when compared to all the classifiers. The classifier stands out producing better results among all the classification methods. This study would be helpful for security agencies and police department to discover data patterns and analyze trending criminal activity from prediction rates.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Data Mining; Classification; Big Data; Crime and Accident
