Detection of Crimes Using Unsupervised Learning Techniques

R. Buli Babu, G. Snehal, P. Aditya Satya Kiran

Abstract


Data mining can be used to detect model crime problems. This paper is about the importance of data mining about its techniques and how we can easily solve the crime. Crime data will be stored in criminal’s database. To analyze the data easily we have data mining technique that is clustering. Clustering is a method to group identical characteristics in which the similarity is maximized or minimized. In clustering techniques also we have different type of algorithm, but in this paper we are using the k-means algorithm and expectation-maximization algorithm. We are using these techniques because these two techniques come under the partition algorithm. Partition algorithm is one of the best methods to solve crimes and to find the similar data and group it. K-means algorithm is used to partition the grouped object based on their means. Expectation-maximization algorithm is the extension of k-means algorithm here we partition the data based on their parameters.

Full Text:

PDF

References


Luís Filipe da Cruz Nassif and Eduardo Raul Hruschka, Document Clustering for Forensic Analysis:

An Approach for Improving Computer Inspection‖, IEEE Transactions On Information Forensics And Security, Vol. 8, No. 1, January 2013.

Manish Gupta1, B.Chandra1 and M. P. Gupta1, 2007 Crime Data Mining for Indian Police Information System.

J. F. Gantz, D. Reinsel, C. Chute, W. Schlichting, J. McArthur, S. Minton, I. Xheneti, A. Toncheva,

and A. Manfrediz, The expanding digital universce: A forecast of worldwide information growth through 2010,‖ Inf. Data, vol. 1, pp. 1–21, 2007.

Faith Ozgul, Claus Atzenbeck, AhmetCelik, Zeki, Erdem, Incorporating data Sources and Methodologies for Crime Data Mining, IEEE proceedings, 2011.

A.Malathi, Dr.S.Santhosh Baboo. D.G. Vaishnav College, Chennai, 2011 Algorithmic Crime Prediction Model Based on the Analysis of Crime Clusters.

J. Mena, “Investigative Data Mining for Security and Criminal Detection”, Butterworth Heineman Press,

pp. 15-16, 2003

Hao Cheng, Kien A. Hua and Khanh Vu, Constrained Locally Weighted Clustering, Journal proceedings of the VLDB Endowment, vol. 1, no.2, 2008

Kadhim B.Swadi al-Janabi. Department of Computer Science. Faculty of Mathematics and Computer Science. University of Kufa/Iraq, 2011 A Proposed Framework for Analyzing Crime Data Set using Decision Tree and Simple K-means Mining Algorithms.

C M Bishop, ―Pattern Recognition and Machine Learning‖ NewYork Springer-Verlag 2006

Kilian Stoffel, Paul Cotofrei and Dong Han, Fuzzy Methods for Forensic Data Analysis, European Journal of Scientific Research, Vol.52 No.4, 2011.

K. Zakir Hussain, M. Durairaj and G. Rabia Jahani Farzana, 2012 Application of Data Mining Techniques for Analyzing Violent Criminal Behavior by Simulation Model

Han, Kamber, Pei, ‖Data Mining: Concepts and Techniques‖, MK Third Edition

C M Bishop, Pattern Recognition and Machine Learning‖ NewYork Springer-Verlag 2006

M. Steinbach, G. Karypis, and V. Kumar. A comparison of document clustering techniques‖. Technical Report 00-034, University of Minnesota, 2000




DOI: https://doi.org/10.11591/APTIKOM.J.CSIT.92

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 APTIKOM Journal on Computer Science and Information Technologies



ISSN: 2722-323X, e-ISSN: 2722-3221

CSIT Stats

 

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