A Review On Crime Analysis And Prediction Using Dbscan Algorithm

Authors

  • Trupti Thakre  Department of Computer Science and Engineering, Babasaheb Ambedkar College of Engineering, Nagpur University, Maharashtra, India
  • Roshani Sharma  Department of Computer Science and Engineering, Babasaheb Ambedkar College of Engineering, Nagpur University, Maharashtra, India
  • Snehal Pote  Department of Computer Science and Engineering, Babasaheb Ambedkar College of Engineering, Nagpur University, Maharashtra, India
  • Avisha Wankhede  Department of Computer Science and Engineering, Babasaheb Ambedkar College of Engineering, Nagpur University, Maharashtra, India
  • Nikita Meshram  Department of Computer Science and Engineering, Babasaheb Ambedkar College of Engineering, Nagpur University, Maharashtra, India

Keywords:

Analysis, Crime, DBSCAN, Existing System, Law Enforcement.

Abstract

Crime is one of the biggest violations that have been not yet completely solved ever since the evolution of human race. The threat will be reduced if a crime prediction and analysis is concerned on particular areas to find if crime is about to happen or not. The existing system is having trouble about data access speed and it is less efficient so to overcome this problem, we proposed a system in which analysis of crime is done also we use DBSCAN algorithm to find out different cluster of crimes. It shows high accuracy for the given dataset and forms effective cluster. Data mining is an approach that can handle large voluminous datasets and used to predict desired patterns. This aspect will be beneficial for both law enforcement and police organization of our country and to give more correct decision also help in safeguarding an area. This application is useful for police and enforcement of law organizations in order to detect crime and for applying preventive measures.

References

  1. Devan M.S (TCS),Crime Analysis and Prediction Using Data Mining Conference Paper , August 2014.
  2. Malathi. A and Dr. S. SanthoshBaboo. Article:an enhanced algorithm to predict a future crime using data mining. International Journal of Computer Applications, 21(1):1–6, May 2011. Published by Foundation of Computer Science.
  3. Anshu Sharma, Raman Kumar, Analysis and Design of an Algorithm Using Data Mining Techniques for Matching and Predicting Crime, IJCST Vol. 4, ISSue 2, AprIl - June 2013.
  4. JyotiAgarwal, RenukaNagpal, RajniSehgal, “Crime Analysis using K-Means Clustering”, International Journal of Computer Applications(0975-8887), Vol. 83, No. 04, 2013
  5. AnishaAgrwal, DhanashreeChoghule, ArpitaAgrwal, Application of Crime Data Minning Using Data Minning Method, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume-4, Issue-5, May.-2016
  6. Veenet Jain, Yogesh Sharma, VaibhavArora, AyushBhatiya, Crime Prediction using Kmeans Algorithm, Global Research and Development Journal for Engineering | Volume 2 | Issue 5 | April 2017.
  7. P. Gera, and R. Vohra, ?Predicting Future Trends in City Crime Using Linear Regression,? IJCSMS (International Journal of Computer Science & Management Studies) Vol. 14, Issue 07Publishing Month: July 2014.
  8. Raghavendhar T.V, Joslin Joshy, Mahaalakshmi R, Ashutosh Soni M, Crime Prediction and Analysis using Clustering Approaches and Regression Methods | IJCAT - International Journal of Computing and Technology| Volume 5| Issue 4, April 2018

International Journal of Scientific Research in Science and Technology

Downloads

Published

2020-03-21

Issue

Volume 5, Issue 7, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Trupti Thakre, Roshani Sharma, Snehal Pote, Avisha Wankhede, Nikita Meshram "A Review On Crime Analysis And Prediction Using Dbscan Algorithm" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 5, Issue 7, pp.444-447, March-April-2020.