Pattern of property crime recidivism by using data mining tool / Nuraqilah Jamian, Nurul Aliah Ghazali and Teh Wardatul Hamraq Ahmad Jamal

• Main Authors: Jamian, Nuraqilah, Ghazali, Nurul Aliah, Ahmad Jamal, Teh Wardatul Hamraq
• Format: Student Project
• Language: English
• Published: 2019
• Subjects: Mathematical statistics. Probabilities; Data processing; Analysis; Analytical methods used in the solution of physical problems
• Online Access: https://ir.uitm.edu.my/id/eprint/50092/1/50092.pdf; https://ir.uitm.edu.my/id/eprint/50092/
• Institution: Universiti Teknologi Mara

Recidivism is the act of continuing to commit crimes after had been punished. Recidivists are not excluded in contributing the number of crimes and tend to repeat the crime because of several factors of life. In the meantime, incarceration has effected the community whom paid the price of high-repeated crimes in social and financial terms, by encountering the lack of public safety, breaking down of social connections, and unavoidable intergenerational poverty. In this study, Frequent Pattern Growth (FP¬ Growth) method was used to identify the pattern of recidivism in property crime and to find the association between property related crime and the number of days to commit recidivism. 73 72 observations from secondary data was used in this study. The data taken from Bureau of Justice Statistics in United States. The pattern that was conducted indicates that most of the offenders had committed burglary as their first type of crime and most of them had repeated the same type of crime. To summarize, FP Growth method is suitable in finding a pattern and should be acknowledged by everyone since it can create rules and producing hidden information in the data especially in predicting recidivism.