Regular frequent crime pattern mining on crime datasets

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation not to happen. Foreseeing violation is one of the worldwide difficulties looking by Law authorization office and it requires tireless endeavors with a specific end goal to limit. In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally fulfills descending conclusion property. Violation designs were not characterized by insights and its distinguishing proof is some-thing other than checking and abridging violations that are comparable in attributes and additionally area on a guide. Violation design is a gathering of at least one violations answered to or on the other hand found by the police.

     

     


  • Keywords


    Crime Pattern; Crime Dataset; Regular-Frequent Patterns; Vertical Data.

  • References


      [1] Vedanayaki M. A study of data mining and social network analysis. Indian Journal of Science and Technology. 2014 Nov; 7(S7):185–187.

      [2] Murugananthan V, Shiva Kumar BL. An adaptive educational data mining technique for mining educational data models in e-learning systems. Indian Journal of Science and Technology. 2016 Jan; 9(3):1–5.

      [3] Azad N, Ranjbar V, Khani D, Moosavi ST. Information disclosure by data mining ap-proach. Indian Journal of Science and Tech-nology. 2012 Apr; 5(4):2593–2602.

      [4] Bruce C, Santos RB. Crime pattern defini-tions for Tactical Analysis. Standards Meth-ods and Technology (SMT) Committee; White paper-2011.

      [5] Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. ACM SIGMOD Internation-al Conference on Management of Data; 1993. P.207–216.

      [6] Agrawal R, Srikanth R. Fast algorithms for mining association rules. VLDB; 1994. p. 489–499.

      [7] G. Vijay Kumar, M. Sreedevi, NVS Pavan Kumar “Mining Regular Patterns in Transac-tional Databases using Vertical Format” In- ternational Journal of Advanced Research in Computer Science, Volume (2), Issue (5) 2011.

      [8] G. Vijay Kumar, Dr. V. Valli Kumari “MaRFI: Maximal Regular Frequent Itemset Mining using a pair of Transaction-ids”, In-ternational Journal of Computer Science & Engineering Technology, Volume 4 No. 7, 2013.

      [9] G. Vijay Kumar, Dr. V. Valli Kumari “IncMaRFI: Mining Maximal Regular Fre-quent Itemsets in incremental databases” In-ternational Journal of Engineering Science and Technology, Volume (5) No (8) 2013.

      [10] Chen H, Chung W, Xu J, Wang G, Qin Y, Chau M. Crime data mining: A general framework and some examples. IEEE Computer Journal. 2004; 37(4):50–56.

      [11] Dandu S, Deekshatulu B, Chandra P. Im-proved algorithm for frequent item sets mining based on apriori and fp-tree. Com-puter Science and Technology Software and Data Engineering Global Journal. 2013; 13(2):1–5.

      [12] Khan NG, Bhaga V. Effective data mining approach for crime-terror pattern detection using clustering algorithm technique. Engi-neering Research and Technology Interna-tional Journal. 2013; 2(4):2043–2048.

      [13] Sreedevi M, Reddy LSS. Mining regular closed patterns in transactional databases. 2013 7th International Conference on Intel-ligent Systems and Control (ISCO); 2013. p. 380–383,

      [14] G. Vijay Kumar, V. Valli Kumari, Parallel and distributed frequent-regular pattern mining using vertical format in large data-bases. IEEE Xplore, IET; 2012. P. 110–114.

      [15] Rashid MM, Karim MR, Jeong BS, Chai HJ. Efficient mining regularly frequent pat-terns in transactional databases. Springer Lecture Notes in Computer Science; 2012. p. 258–271.

      [16] Rashid MM, Karim MR, Jeong BS, Chai HJ. Efficient mining regularly frequent pat-terns in transactional databases. Springer Lecture Notes in Computer Science; 2012. p. 258–271.

      [17] Tanbeer SK, Chowdhury FA. RP-Tree: A tree structure to discover regular patterns in transactional database. IDEAL 2008, Volume 5236 of the series Lecture notes in Computer Science; 2008. p. 193–200.

      [18] Usha D, Ramesh Kumar K. A complete survey on application of frequent pattern mining and association rule mining on crime pattern mining. International Journal of Advances in Computer Science and Technology. 2014; 3(4):264–275.

      [19] Lee JR, Kim SA, Yoo JW & Kang YK (2007), The present status of diabetes education and the role recognition as a diabetes educator of nurses in korea. Diabetes Research and Clinical Practice 77, 199–204.


 

View

Download

Article ID: 11438
 
DOI: 10.14419/ijet.v7i2.7.11438




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.