Comprehensive and Comparative Study of Efficient Location Tracking Based on Apriori and Dijkstra Algorithms

  • D. Venkata SubramanianEmail author
  • R. Sugumar
  • N. Dhipikha
  • R. Vinothini
  • S. Kavitha
  • A. Harsha Anchaliya
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)


With the advent of Google maps, pointing the precise location and tracking the movement of objects, people and materials have become an integral part of SCM System. The ERP systems include Logistics and Management as core functionalities along with Artificial Intelligent Systems to facilitate positioning and tracking of materials. Similar to material tracking, it is also important to track the location and movement of the person which is a huge demand in present era. However, the designers of such larger ERP systems use patented protocols for material location identification, predicting the shortest distance and tracking the precise location of cargo when they are on the move. The similar techniques neither fully adopted nor applied with humans. This research paper is aimed at finding out the most efficient route by comparing the most popular Apriori and the Dijkstra algorithms. Apriori algorithm involves supervised mining using association rules which can be used for finding right paths whereas the same can be estimated by the Dijkstra’s algorithm, taking the connecting nodes in the graph.


Supply chain management ERP Apriori algorithm Dijkstra’s algorithm Location tracking Dataset Prediction Location Tracking Database GPS Notification 


  1. 1.
    Ponniah, P.: Data Warehousing Fundamentals—A Comprehensive Guide for IT Professionals, Ist edn. Glorious Printers, New Delhi (2007). ISBN-81-265-0919-8, second reprintGoogle Scholar
  2. 2.
    An Introduction to Data Mining, Review.
  3. 3.
    A Tutorial on Clustering Algorithms, Review.
  4. 4.
    Mining Frequent data sets-Apriori algorithm.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Zhou, Y., Wan, W., Liu, J. Cai, L.: Mining association rules based on an improved Apriori algorithm. IEEE (2010). 978-1-4244-5858-5/10/Google Scholar
  9. 9.
    Fang, L.: The study on the application of data mining based on association rules. In: International Conference on Communication Systems and Network Technologies (IEEE), pp. 477–480, May 2012Google Scholar
  10. 10.
    Yu, C., Ying, X.: Research and improvement of Apriori algorithm for rules. In: 2nd International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4, May 2010Google Scholar
  11. 11.
    Singh, J., Ram, H., Sodhi, J.S.: Improving efficiency of Apriori algorithm using transaction reduction. Proc. Int. J. Sci. Res. Publ. (IJSRP) 3(1), 1–4 (2013). ISSN 2250-3153Google Scholar
  12. 12.
    Waghela, P.P.D.: Comparative study of association rule mining algorithms. In: Proceeding of UNIASCIT, vol. 2, no. 1, pp. 170–172 (2012). ISSN 2250-0987Google Scholar
  13. 13.
    Geetha, K., Mohiddin, S.: An efficient data mining technique for generating frequent item sets. Proc. IJARCSSE 3(4), 571–575 (2013). ISSN 2277-128XGoogle Scholar
  14. 14.
    Dhanda, M., Guglani, S., Gupta, G.: Mining efficient association rules through Apriori algorithm using attributes. Proc. IJCST 2(3), 342–344 (2011). ISSN 0876-8491Google Scholar
  15. 15.
    Nagpal, S.: Improved Apriori algorithm using logarithmic decoding and pruning. Proc. Int. J. Eng. Res. Appl. 2(3), 2569–2572 (2012). ISSN 2248-9622MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • D. Venkata Subramanian
    • 1
    Email author
  • R. Sugumar
    • 1
  • N. Dhipikha
    • 1
  • R. Vinothini
    • 1
  • S. Kavitha
    • 1
  • A. Harsha Anchaliya
    • 1
  1. 1.Department of Computer Science and EngineeringVelammal Institute of TechnologyChennaiIndia

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