Open Access   Article Go Back

Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique

S. Das1 , A. Das2 , A.U. Islam3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-8 , Page no. 356-363, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i8.356363

Online published on Aug 31, 2018

Copyright © S. Das, A. Das, A.U. Islam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S. Das, A. Das, A.U. Islam, “Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.356-363, 2018.

MLA Style Citation: S. Das, A. Das, A.U. Islam "Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique." International Journal of Computer Sciences and Engineering 6.8 (2018): 356-363.

APA Style Citation: S. Das, A. Das, A.U. Islam, (2018). Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique. International Journal of Computer Sciences and Engineering, 6(8), 356-363.

BibTex Style Citation:
@article{Das_2018,
author = {S. Das, A. Das, A.U. Islam},
title = {Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2018},
volume = {6},
Issue = {8},
month = {8},
year = {2018},
issn = {2347-2693},
pages = {356-363},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2702},
doi = {https://doi.org/10.26438/ijcse/v6i8.356363}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.356363}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2702
TI - Finding Patterns in Crime Against Women Using a Fuzzy Clustering Technique
T2 - International Journal of Computer Sciences and Engineering
AU - S. Das, A. Das, A.U. Islam
PY - 2018
DA - 2018/08/31
PB - IJCSE, Indore, INDIA
SP - 356-363
IS - 8
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
527 310 downloads 284 downloads
  
  
           

Abstract

In most of the recent works pertaining to crime analysis traditional hard clustering techniques are seen to be applied for obtaining the intensity of crime in a particular region. Such clustering techniques which are based on crisp set theory are unable to deal with partial belongingness and as a result it is not possible to find regions partially belonging to multiple clusters with different crime intensities. Keeping this limitation of hard clustering techniques in view we will apply a fuzzy clustering technique which can deal the situations pertaining to partial belongingness, on a dataset of crime against women to reveal some important patterns in it.

Key-Words / Index Term

fuzzy clustering, crime against women, YFCM, FCM, patterns

References

[1] S. Das and H. K. Baruah., “A New Method to Remove Dependence of Fuzzy C-Means Clustering Technique on Random Initialization”, International Journal of Research in Advent Technology, Vol. 2, Issue.1, pp.322-330, 2014.
[2] J. C. Bezdek, , “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York, 1981.
[3] M. Chau, J. Xu, and H. Chen,“Extracting meaningful entities from police narrative reports”, Proceedings of the National Conference for Digital Government Research, Los Angeles, California, USA, pp.1-5, 2002.
[4] J.S. De Bruin, T.K Cocx., W.A Kosters., J Laros. and J.N Kok., “Data mining approaches to criminal carrer analysis”, Proceedings of the Sixth International Conference on Data Mining, pp.171-177, 2006.
[5] D. K. Tayal, A Jain. and S. Arora, “Crime detection and criminal identification in India using data mining techniques”, AI & SOCIET, Vol.30, Issue. 1, pp.117–127, 2015.
[6] A. Alkhaibari and P. T. Chung, “Cluster analysis for reducing city crime rates”, Systems, Applications and Technology Conference (LISAT), Long Island, NY, USA, 2017.
[7] C. Chauhan and S Sehgal., “A review: Crime analysis using data mining techniques and algorithms”, International Conference on Computing, Communication and Automation (ICCCA), Uttar Pradesh,India,2017.
[8] L. S Thota., M. Alalyan, A. A Khalid., F. Fathima, S. B. Changalasetty. and M, Shiblee. “Cluster based zoning of crime info”, 2nd International Conference on Anti-Cyber Crimes (ICACC), 2017.
[9] T. Aljrees, D. Shi, D. Windridge and W. Wong, “Criminal pattern identification based on modified K-means clustering”, IEEE International Conference on Machine Learning and Cybernetics (ICMLC),Jeju, South Korea, 2017.
[10] L. A., Zadeh “Fuzzy Sets”, Information and Control, Vol. 8, Issue. 3, pp.338-353, 1965.
[11] G. W. Dewit, “Underwriting and Uncertainty”, Insurance: Mathematics and Economics, Vol. 1, Issue. 4, pp. 277-285, 1982.
[12] J. Lemiare, “Fuzzy Insurance”, Astin Bulletin, Vol. 20, Issue. 1, pp. 33-55, 1990.
[13] K. Ostaszewski, “An Investigation into Possible Applications of Fuzzy Sets Methods in Actuarial Science”, Society of Actuaries, Schaumburg, Illinois, 1993.
[14] P. Pardeshi and U. Patil, “ Fuzzy Association Rule Mining-A Survey”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6,pp.13-18, 2017.
[15] L. Zheng and X. He, “Classification Techniques in Pattern Recognition”, Conference Proceedings of 13th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, ISBN 80-903100-8-7 WSCG, Science Press ,Australia, pp. 77-88, 2005.
[16] T. SenthilSelvi and R. Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.15-19, 2018.
[17] R. A. Derrig and K. M Ostaszewski., “Fuzzy Techniques of Pattern Recognition in Risk and Claim Classification”, Journal of Risk and Insurance, Vol. 62, Issue. 3, pp.447-482, 1995.
[18] S. Das, “Pattern Recognition using the Fuzzy c-means Technique”, International Journal of Energy, Information and Communications, Vol. 4, Issue 1, pp.1-14,2013.
[19] S. Das and H. K. Baruah, “Application of Fuzzy C-Means Clustering Technique in Vehicular Pollution”, Journal of Process Management – New Technologies, Vol. 1, Issue. 3, pp.96-107, 2013.
[20] D. E Gustafson. and W. C. Kessel, “Fuzzy clustering with a fuzzy covariance matrix”, Proc. IEEE CDC, San Diego, CA, USA, pp.761- 766, 1979.
[21] S. Das and H. K. Baruah, “A Comparison of Two Fuzzy Clustering Techniques”, Journal of Process Management – New Technologies, Vol. 1, Issue. 4, pp.1-15,2013.
[22] R.R. Yager and D.P. Filev “Approximate Clustering Via the Mountain Method”, Tech. Report #MII-1305, Machine Intelligence Institute, Iona College, New Rochelle, NY, 1992.
[23] S.L Chiu., “Fuzzy Model Identification Based on Cluster Estimation”, Journal of Intelligent and Fuzzy Systems, Vol.2, pp.267-278,1994.
[24] F. Yuan, Z.H Meng., H.X. Zhang and C.R Dong., “A New Algorithm to Get the Initial Centroids”, Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pp.26-29, 2004.
[25] S. Das and H. K. Baruah, “An Approach to Remove the Effect of Random Initialization from Fuzzy C-Means Clustering Technique”, Journal of Process Management – New Technologies, Vol. 2, Issue. 1, pp.23-30,2014.