Crime Analysis Based on K-Means Clustering

EasyChair Preprint 6205

Abstract

In today’s world, criminals and terrorists are technologically sophisticated. All the government gives higher priority to prevent and reduce crimes. Crime analysis is a collection of strategies that allow the police forces to become more effective through better knowledge. The basic objective of any clustering algorithm is to cluster or group similar data points into a single cluster. Our proposed framework aims to forecast the probability of crime occurring in a city by analyzing the crime dataset and visualizing the findings for better comprehension. This research is achieved by using a clustering algorithm of k means that group related objects into clusters, the proposed research work mainly focuses on predicting the region with higher crime rates.

Keyphrases: Clustering, Crime Analysis, K-means, unsupervised algorithm

@booklet{EasyChair:6205,
  author    = {Nithin Joseph},
  title     = {Crime Analysis Based on K-Means Clustering},
  howpublished = {EasyChair Preprint 6205},
  year      = {EasyChair, 2021}}