Crime Trend Analysis Using Data Mining Technique

An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools, and methods in analyzing, classifying, and predicting crimes. These paper clusters municipalities in Surigao del Norte using K-Means algorithm. This is instrumental in finding identical traits, patterns, and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017. Simulation results showed that the Surigao City topped as the municipality in Surigao del Norte, Philippines with the most number of reported index and non-index crimes which belongs to Cluster 1 as depicted by the algorithm. It is suggested that the output of this study may be used as input for new studies about crime identification. Future researchers may also utilize other data mining techniques supported by a better accuracy result.