Advertisement

Sequential mining of real time moving object by using fast frequence pattern algorithm

  • D. Venkatavara Prasad
  • N. Venkatesvara Rao
  • M. Sugumaran
Article
  • 42 Downloads

Abstract

In the field of image processing, data mining technique is being implemented in various concepts. Generally, the management of video content with data mining technique became an essential part since there is an increase in the advancement of multimedia and networking technology. Previously, there are certain algorithm such as Apriori and frequency pattern growth algorithm for video management. In this paper, a novel fast frequency pattern algorithm is designed to find the high priority pattern with minimum time. In this concept the data mining process is carried out in vertical format in order to find the pattern with high priority. The simulated results are compared with the existing data mining algorithms and it is found that the proposed algorithm is efficient in aspect of time and memory size.

Keywords

Data mining FP growth Apriori FFP MST MCT 

References

  1. 1.
    Harsh, M.: Sequential  mining of  multimedia images by using SPADE Algorithm. Int. J. Comput. Sci. Inf. Tech. 4(6), 791–795 (2013)Google Scholar
  2. 2.
    Berjon, D., Cuevas, C., Moran, F., Garcia, N.: GPU-based implementation of an optimized nonparametric background modelling for real-time moving object detection. IEEE Trans. Consumer Electron. 59(2), 361–369 (2013)CrossRefGoogle Scholar
  3. 3.
    Saravanan, D., Srinivasan, S.: Video image retrieval using data mining techniques. J. Comput. Appl. 1, 39–42 (2012)Google Scholar
  4. 4.
    Nasreen, S., Azam, M.A., Shehzad, K., Naeem, U., Ghazanfar, M.A.: Frequent pattern mining algorithms for finding associated frequent patterns for data streams: a survey. Procedia Comput. Sci. 37, 109–116 (2014)CrossRefGoogle Scholar
  5. 5.
    Traore, B.B., Kamsu-Foguem, B., Tangara, F.: Data mining techniques on satellite images for discovery of risk areas. Exp. Syst. Appl. 72, 443–456 (2017)CrossRefGoogle Scholar
  6. 6.
    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., & Hsu, M.C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)CrossRefGoogle Scholar
  7. 7.
    Derakhshan, R., & Ahmadi, A.: A new data structure to enhance the speed of frequent pattern mining. In: Iranian Conference on Electrical Engineering (ICEE), May 2017, pp. 2128–2133. IEEE (2017)Google Scholar
  8. 8.
    Lin, T. Y.: Very fast frequent itemset mining: simplicial complex methods. In: IEEE International Conference on Big Data (Big Data), December 2016, pp. 1946–1949. IEEE (2016)Google Scholar
  9. 9.
    Dwivedi, N., & Satti, S. R.: Set and array based hybrid data structure solution for Frequent Pattern Mining. In: Tenth International Conference on Digital Information Management (ICDIM),  October 2015, pp. 14–19. IEEE (2015)Google Scholar
  10. 10.
    Lv, D., Fu, B., Sun, X., Qiu, H., Liu, X., & Zhang, Y.: Efficient fast updated frequent pattern tree algorithm and its parallel implementation. In: 2nd International Conference on Image, Vision and Computing (ICIVC), June 2017, pp. 970–974. IEEE (2017)Google Scholar
  11. 11.
    Kumar, V., & Valli Kumari, V.: Incremental mining for regular frequent patterns in vertical format. Int. J. Eng. Tech. 5(2), 1506–1511 (2013)Google Scholar
  12. 12.
    Chen, C., Lin, C. X., Fredrikson, M., Christodorescu, M., Yan, X., & Han, J.: Mining graph patterns efficiently via randomized summaries. Proc. VLDB Endow. 2(1), 742–753 (2009)CrossRefGoogle Scholar
  13. 13.
    Saravanan, D., & Vengatesh, K. J.: Video content reterival using historgram clustering technique. Proc. Comp. Sci. 50, 560–565 (2015)CrossRefGoogle Scholar
  14. 14.
    Mohsin, Md, Rayhan Ahmed, Md & Ahmed, T.: Closed frequent pattern mining using vertical data format: depth first approach. IJSSET 2(3), 230–238 (2016)Google Scholar
  15. 15.
    Chouhan, P., & Tiwari, M.: Image retrieval using data mining and image processing techniques. IJIREEICE 3, 53–58 (2015).  https://doi.org/10.17148/IJIREEICE.2015.31212 CrossRefGoogle Scholar
  16. 16.
    Girija, K., Maheswaran, R., & Sabarinathan, P.: Copy frame detection in video using data mining techniques. In: International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), April 2014. pp. 406–411. IEEE (2014)Google Scholar
  17. 17.
    Han, J., Cheng, H., Xin, D., & Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Li, Y., Xu, J., Chen, L.: A new closed frequent itemsets mining algorithm based on GPU. In: 2015 Third International Conference on Advanced Cloud and Big Data, pp. 291–295. IEEE (2015)Google Scholar
  19. 19.
    Ganesh, C., Sathyabhama, B., & Geetha, D. T.: Fast frequent pattern mining using vertical data format for knowledge discovery. Int. J. Eng. Res. Manag.Technol. 5, 141–149 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringSSN College of EngineeringChennaiIndia
  2. 2.JNTUKKakinadaIndia
  3. 3.Kings Engineering CollegeChennaiIndia
  4. 4.CSE DepartmentPondicherry Engineering CollegePondicherryIndia

Personalised recommendations