Identification of Accurate Classification Technique for Crime Investigation Using Ensemble Approach
DOI:
https://doi.org/10.26438/ijcse/v7i8.137143Keywords:
Crime investigation, Crime Prediction, Data Mining, Ensemble approachAbstract
Recently, it`s observed that the crime is increasing across the world very rapidly and some technique is required for analysis of the crime data. Analysis of the crime data can be done through data mining (DM). DM techniques are applied to crime data for predicting features that affect the high crime rate. Using the method of data mining on previously collected data for predicting the features responsible for the crime in a locality or area, the Police Department and the Crimes Record Bureau Police Department may take the required measures to reduce the likelihood of the crime. In the current work, a new machine learning ensemble algorithm is opted for predicting feature that affects a high crime rate. It helps the police and citizens to take necessary and required action in decreasing the crimes rate. The ensemble algorithm can predict more accurate and significant features with higher accuracy and efficiency.
References
[1] OdedMaimon, LiorRokach, “The Data Mining and Knowledge Discovery Handbook”, Springer 2005, Page 6
[2] Han, Jiawei et.al “Data Mining”, Second Edition, Page 285
[3] Mugdha Sharma, “Z-Crime: A Data Mining Tool for the Detection of Suspicious Criminal Activities based on the Decision Tree”, International Conference on Data Mining and Intelligent Computing, pp. 1-6, 2014
[4] Ehab Hamdy, Ammar Adl, Aboul Ella Hassanien, Osman Hegazy and Tai-Hoon Kim, “Criminal Act Detection and Identification Model”, Proceedings of 7 th International Conference on Advanced Communication and Networking, pp. 79-83, 2015
[5] Kaumalee Bogahawatte and Shalinda Adikari, “Intelligent Criminal Identification System”, Proceedings of 8th IEEE International Conference on Computer Science and Education, pp. 633-638, 2013.
[6] Jyoti Agarwal, Renuka Nagpal and Rajni Sehgal, “Crime Analysis using K-Means Clustering”, International Journal of Computer Applications, Vol. 83, No. 4, pp. 1-4, 2013.
[7] Prajakta Yerpude, Vaishnavi Gudur. “Predictive modelling of crime dataset using data mining”. In international journal of data mining & knowledge management process, vol.7, pp.43-58, 2017.
[8] Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J. Laros and Joost N. Kok, “Data Mining Approaches to Criminal Career Analysis”, Proceedings of 6 th IEEE International Conference on Data Mining, pp. 1-7, 2006.
[9] H. Chen, W. Chung, J.J. Xu, G. Wang, Y. Qin and M. Chau, “Crime Data Mining: a General Framework and Some Examples”, Computer, Vol. 37, No. 4, pp. 50-56, 2004.
[10] Sadhna shrama, sanjiv sharma, “ a compartive study of crime investigation using data mining approaches”, International Journal for Research in Applied Science & Engineering Technology,Vol.7,pp. 2073-2079,2019.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
