Crime Analysis using K-Means Clustering Algorithm - A Case Study: Police Departments, Wad Madani, Sudan (2016-2018) - Babikir, Gais Alhadi; Suliman, Tarig Khalid; Ahmed, Awadallah Mohammed (Gezira Journal of Engineering and Applied Sciences; Vol 16 No 2 (2021), 2023)
In today's world, recently global security is one aspect that has a high priority and importance by politicians and governments around the world, it aims to reduce the occurrence of crimes. Therefore, many studies aimed at knowing the types of crimes, the manner of their occurrence, the search for criminals, study their personal qualities and public behavior since each crime shares the criminals who commit it in certain character traits and patterns. This study aims to find the relationships between crimes and the characteristics of criminals who committed the crimes (such as age, gender, location, and job) in order to help Police Departments to make the right decisions through crime investigation. In this paper, the k-means clustering algorithm has been used as a data mining technique to discover the relationships between crime and criminal characteristics. Where statistical descriptive analysis was used for all processes, also we used WEKA as a tool to implement the k-means algorithm. It should be noted that the data of this study have been collected from three Police Departments in Wad Madani city, Sudan for criminal records for three years (2016, 2017, and 2018). The analysis of the results showed that there are some crimes related to the specific age of criminals. We also found that each region has its own crimes which are different from the other region. Moreover, the most common crimes committed by males: are fraud, scandalous acts, public employee objection, check, and public threat. And, the most common crimes committed by females: are dealing with alcohol, abuse, and slander. Furthermore, the analysis of the result showed that the most common crimes committed by students are sexual, theft, and violence.