There are nearly thousands of crimes that happen every day. There are many algorithms for calculating an area's
crime rate but there is no best algorithm for calculating an area's crime rate, depending on the algorithm's accuracy and time
complexity. So, we've taken four unsupervised clustering algorithms K means clustering, agglomerative clustering, gaussian
clustering, density-based spatial clustering algorithms to compare them based on the accuracy of the algorithms on a given
collection of data to figure out the best algorithm to figure out the crime rate of a given region. This paper essentially presents a
comparative study of all four clustering algorithms, and it is found that k means clustering algorithm is the best clustering
algorithm for calculating an area's crime rate on the given data set.
