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  Mining Popular Crime Patterns from Crime Datasets  
  Authors : BVS. Varma; V. Valli Kumari
  Cite as:


Criminal investigation plays an important role in law enforcement agencies while analyzing crimes. This can help in finding suspects and for better attribution of past crimes. So the use of pattern based approaches will have the potential to assist crime analyze experts in discovering new patterns of criminal activities. So the research is extended in this area in finding new crime patterns. Since frequent pattern mining problem has been introduced, researchers have been developed many ways and also extended to different useful emerging patterns such as closed, maximal, cyclic, periodic, and popular patterns. In this paper, we introduced popular crime patterns which give the popularity of each crime or incident among the entire crime database. The process used for mining popular crime patterns is discussed as PCrime-growth algorithm. The compact data structure called PCrime-tree is implemented in this algorithm. Finally our experiment results have been shown which gives information of compact, space efficient and time efficient of our proposed algorithm.


Published In : IJCSN Journal Volume 4, Issue 5

Date of Publication : October 2015

Pages : 741 - 748

Figures :03

Tables : 01

Publication Link : Mining Popular Crime Patterns from Crime Datasets




B.V.S.Varma : is Associate Professor in the department of Computer Science & Engineering at Ideal Institute of Technology, India. He obtained M.Tech degree in CSE from Dr. M.G.R.University and B.Tech in CSE from Madras University. He has 13 years of Teaching Experience He research interest includes Data Mining.

V.Valli Kumari : is professor in the department of Computer Science & Systems Engineering and Director of Computer Center at Andhra University, India. She has received best Researcher Award from the same university. She has 17 years of teaching experience and guided several Ph.Ds. she is an active researcher and executed several research projects. She has published more than 120 research papers in international conferences and journals, authored several book chapters and chaired several international conferences.








Crime Patterns

Popular Patterns

Crime databases


In this paper we introduced a new algorithm called PCrime-growth algorithm that finds popular crime patterns from Crime datasets. With the first database scan it constructs PCrime-tree and also computes support of a crime, maximum crime transaction length and popularity of each crime. After that the algorithm performs for super-pattern popularity for unpopular crimes to prune the crime database. After second scan it’s going to compute length of each crime transaction by eliminates unpopular crimes and will extracts popular crime patterns from the crime databases. Our experimental results showed that our PCrime-tree is time and space efficient for both sparse and dense datasets. In addition these results are also affected on crime datasets on construction of PCrime-tree and mining of popular crime patterns to be time efficient.










[1] N. G. Khan, V. Bhaga, Effective data mining approach for crime-terrorpattern detection using clustering algorithm technique, Engineering Research and Technology International Journal Vol 2 (4) (2013), pp. 2043–2048. [2] H. Chen, W. Chung, J. Xu, G. Wang, Y. Qin, M. Chau, Crime data mining: A general framework and some examples, IEEE Computer Journal 37 (4) (2004) 50–56. [3] Mohammad, J. Mohsen, E. Martin, G. Uwe, F. Richard, Crimewalker: A recommendation model for suspect investigation, in: Proc. fifth ACM conference on Recommender systems, ACM, 2011, pp. 1–8. [4] O. Isafiade, A. Bagula, Citisafe: Adaptive spatial pattern knowledge using fp-growth algorithm for crime situation recognition, in: Proc. IEEE International Conference on Ubiquitous Intelligence and Computing, IEEE, 2013, pp. 551–556. [5] P. Phillips, I. Lee, Mining co-distribution patterns for large crime datasets, Expert Systems with Applications International Journal 39 (14) (2012) 11556–11563. [6] D. Wang, W. Ding, H. Lo, T. Stepinski, J. Salazar, M. Morabito, Crime hotspot mapping using the crime related factors- a spatial data mining approach, Applied Intelligence Journal 39 (4) (2013) 772–781. [7] Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB, 1994, pp. 487–499 (1994). [8] Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12 (2000). [9] Lee, Y.-K., Kim, W.-Y., Cai, Y.D., Han, J.: CoMine: efficient mining of correlated patterns. In: IEEE ICDM 2003, pp. 581–584 (2003). [10] Rasheed, F., Alshalalfa, M., Alhajj, R.: Efficient periodicity mining in time series databases using suffix trees. IEEE TKDE 23(1), 79–94 (2011). [11] Zhang, M., Kao, B., Cheung, D.W., Yip, K.Y.: Mining periodic patterns with gap requirement from sequences. ACM TKDD, 1(2), art. 7 (2007). [12] Lakshmanan,L.V.S., Leung, C.K.- S.,Ng,R.T.:Efficient dynamic mining of constrained frequent sets. ACM TODS 28(4), 337–389 (2003). [13] Leung, C.K.-S., Sun, L.: A new class of constraints for constrained frequent pattern mining. In: ACM SAC 2012, pp. 199–204 (2012). [14] Xiong, H., Tan, P.-N., Kumar, V.: Hyperclique pattern discovery. Data Mining and Knowledge Discovery 13(2), 219–242 (2006). [15] Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. DKE 59(3), 603–626 (2006). [16] Leung C.K.-S., Tanbeer S.K.: Mining Popular Patterns from Transactional Databases. Springer DaWak 2012, 291-302 (2012). [17] Leung C.K.-S., Tanbeer S.K.: Finding Popular Friends in Social Networks. IEEE Second International Conference on Cloud and Green Computing, 501-508 (2012). [18] Omowunmi Isafiade, Antoine Bagula, Sonia Berman.: A Revised Frequenrt Pattern Model for Crime Situation Recognition Based on Floor-Ceil Quartile Function. Elsevier Science Direct, Procedia Computer Science 55 (2015), 251-260. [19] Brown, D.E. The regional crime analysis program (RECAP): A frame work for mining data to catch criminals," in Proceedings of the