Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Authors

  • M Ramzan Begam
  • P Sengottuvelan

DOI:

https://doi.org/10.14419/ijet.v7i3.27.17662

Published:

2018-08-15

Keywords:

Data mining, crime data analysis, clustering, crime-patterns, crime profiling.

Abstract

Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule mining algorithm to solve the crime problems. Also to use the sentence case observer technique for knowledge discovery from the crime records for proper case identification from sentence case records and to help increase the predictive accuracy. Crime examines a developing method and identifying the field in law implementation without standard definitions for correct judgment. With the expanding utilization of the clustering automated frameworks to track crimes, information examiners helping the law implementation officers and analysts to accelerate the way toward measuring crimes. The main contribution is to analyses the attribute case with relative sentences of count word analyzes factor for improving the crime prediction for categorizing crime type.

 

 

References

[1] Han J & Kamber M, Data mining: concepts and techniques, Jim Gray, Series Editor Morgan Kaufmann Publishers, (2000).

[2] Rodriguez JJ, Kuncheva LI & Alonso CJ, “Rotation forest: a new classifier ensemble methodâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.28, (2006), pp.1619-1630.

[3] Bhattacharyya S, Jha S, Tharakunnel K & Westland JC, “Data mining for credit card fraud: a comparative studyâ€, Decision Support Systems, Vol.50, No.3,(2011), pp. 602-613.

[4] Nath SV, “Crime pattern detection using data miningâ€, Proceedings of the IEEE/WIC/ACM Web Intelligence and Intelligent Agent Technology international conference, (2006), pp.41-44.

[5] Ferligo A, “Recent developments in cluster analysisâ€, Telecommunication Systems, Vol.1, No.4, (2003), 205-220.

[6] Pham DT, Otri S, Afifty A, Mahmuddin M & Al-Jabbour H, “Data clustering using the Bees algorithmâ€, proceedings of 40th CRIP International Manufacturing Systems Seminar, (2006).

[7] Chen H, Chung W, Xu JJ, Wang G, Qin Y & Chau M, “Crime data mining: a general framework and some examplesâ€, computer, Vol.37, No.4, (2004), pp.50-56.

[8] Arora S & Chana I, “A survey of clustering techniques for big data analysisâ€, 5th International Conference-Confluence The Next Generation Information Technology Summit, (2014), pp.59-65.

[9] Kaur PJ, “A survey of clustering techniques and algorithmsâ€, 2nd International Conference on Computing for Sustainable Global Development (INDIACom), (2015), pp.304-307.

[10] Perry WL, McInnis B, Price CC, Smith SC & Hollywood JS, Predictive Policing The Role of Crime Forecasting in Law, RAND Corporation, (2013).

[11] Bsoul Q, Salim J & Zakaria LQ, “An intelligent document clustering approach to detect crime patternsâ€, Procedia Technology, Vol.11, (2013), pp.1181-1187.

[12] Gera P & Vohra R, “City Crime Profiling Using Cluster Analysisâ€, International Journal of Computer Science and Information Technologies, Vol.5, No.4,(2014), pp.5145-5148.

[13] Ying K, Chang M, Chiarella AF & Heh JS, “Clustering students based on their annotations of a digital textâ€, IEEE Fourth International Conference on Technology for Education (T4E), (2012), pp.20-25.

[14] Joshi S & Nigam B, “Categorizing the document using multi-class classification in data miningâ€, International Conference on Computational Intelligence and Communication Systems, (2011).

[15] Gera P & Vohra R, “Predicting Future Trends in City Crime Using Linear Regressionâ€, International Journal of Computer Science & Management Studies, Vol. 14, No.07, (2014).

[16] Ding L, Steil D, Hudnall M, Dixon B, Smith R, Brown D & Parrish A, “PerpSearch: an integrated crime detection systemâ€, IEEE International Conference on Intelligence and Security Informatics, (2009), pp.161-163.

[17] Bogahawatte K & Adikari S, “Intelligent criminal identification systemâ€, IEEE 2013 The 8th International Conference on Computer Science & Education, (2013).

[18] Babakura A, Sulaiman N & Yusuf M, “Improved method of classification algorithms for crime predictionâ€, International Symposium on Biometrics and Security Technologies, (2014).

[19] Sathyadevan S & Gangadharan S, “Crime analysis and prediction using data miningâ€, First International Conference on Networks & Soft Computing (ICNSC), (2014), pp.406-412.

[20] Saxena R, “Educational Data Mining: Performance Evaluation of Decision Tree and Clustering Techniques Using WEKA Platform†Business Informatics as International Journal of Computer Science, Vol.15, No.2, (2015).

[21] James Manoharan J, Hari Ganesh S & LovelinPonnFelcia M, “Discovering Student's Academic Performance Based on GPA using k-Means Clustering Algorithmâ€, IEEE World Congress on Computing and Communication Technologies, (2013).

[22] Sivaranjani S, Sivakumar S & Aasha M, “Crime prediction and forecasting in Tamilnadu using clustering approachesâ€, Emerging Technological Trends (ICETT), (2014).

[23] Dutt A, Aghabozrgi S, Ismail MAB & Mahroeian H, “Clustering algorithms applied in educational data miningâ€, International Journal of Information and Electronics Engineering, Vol.5, No.2,(2015), pp.112-116.

[24] Kaur S & Singh W, “Systematic Review of Crime Data Miningâ€, International Journal of Advanced Research in Computer Science, Vol.8, No.5, (2017).

[25] Mahmud MS, Meesad P & Sodsee S, “An evaluation of computational intelligence in credit card fraud detectionâ€, International Computer Science and Engineering Conference (ICSEC), (2016), pp.1-6.

[26] G Mussabekova, S Chakanova, A Boranbayeva, A Utebayeva, K Kazybaeva, K Alshynbaev (2018). Structural conceptual model of forming readiness for innovative activity of future teachers in general education school. Opción, Año 33. 217-240

View Full Article: