Abstract:
In the digital era, police forces have access to quickly expanding sources of information. The enormous increase in the amount of available data has made the use of data mining techniques essential in finding important patterns. This research, on the data collected from kaggle competition, San Francisco Crime Classification, will take combinations of type of crime and time to determine locations where it is more likely to occur. This will help in planning preventive measures. Though the kaggle competition expected the participants to determine the type of crime occurring given they have knowledge about the time and location, in this paper the outcome is slightly changed since many studies have been done on the before mentioned problem. To predict the outcome i.e. given the type of crime and time, at which location(s) it’s more likely to occur, in this study the technique used will be k-means clustering. For performing K-means clustering Weka data mining tool was used. Also the different types of crimes were analyzed on the basis of days in a week and graphs are provided to understand the relation between the rates of crime and the day it is happening on. In the results the location(s) of the most prominent crimes and time are given which will help the police forces to take preventive measures.
