Optimization Method to Find the Priority Group to be Vaccinated Against Corona Virus

V. G. Rajalekshmi1


1Department of Mathematics, S.D.College, Alappuzha, Kerala, India.

Abstract: Current evidence shows that the corona virus spreads mainly between people who are in close contact with each other. More than 3.9 million people died globally due to covid-19. There are various types of vaccine produced against this virus. But the vaccine produced so far is insufficient to vaccinate the whole society. Production of vaccine is in its maximum capacity. Now it is better to give vaccine in a priority order. Here an optimization model is given using Dynamic programming to find the set of people in a region to be vaccinated first.
Keywords: Frequent Item Sets, State Variable, Covid-19, Dynamic Programming.


Cite this article as: V. G. Rajalekshmi, Optimization Method to Find the Priority Group to be Vaccinated Against Corona Virus, Int. J. Math. And Appl., vol. 9, no. 3, 2021, pp. 49-53.

References
  1. R. Agarwal, T. Imielinski and A. Swami, Mining association rules between sets of items in large databases, In Proceedings of the ACM SIG MOD Conference on Management of Data, (1993), 207-2016.
  2. R. Agarwal and R. Srikant, Fast algorithms for mining association rules, In VLDB 94, 487-499.
  3. R. Agarwal, C. Aggaral and V. V. V. Prasad, A tree projection algorithm for generation of frequent itemsets, Int. J. of Parallel and Distributed Computing (Special issue on High performance Data Mining), (2000).
  4. R. Agarwal, J. Gehrike, D. Gunopulos and P. Raghavan, Atomatic subspace clustering oof high dimensional data for data mining applications, In SIG MOD 98, 94-105.
  5. R. J. Bayardo, Efficiently mining long patterns from databases, In SIGMOD 98, 85-93.
  6. B. BLiU, W. Hsu and Y. Ma, Integrating Classification association rule mining, In KDO'98, 80-86.
  7. H. Manimala, H. Toivonen and A. I. Verkamo, Efficient algorithms for discovering association rules, In KDD'94, 181-192.
  8. Jiawei Han, Michelin Kamber and Jian Pei, Data mining concept and techniques, Third Edition, Elsevier Science, (2011).
  9. Jain Pei, Jiawei Han, Hongjum Lu, Shojiro Nishio, Shiwei Tang, Dongquig Yang, H-Mine: Hyper-structure mining of frequent patterns in large data base, Proceedings 2001 IEEE International Conference on Data Mining, (2001), https://doi.org/10.1109/ICDM.2001.989550.
  10. J. Han, J. Pei and Y. Yiri, Mining frequent patterns without candidate generation, In SIGMOD'00, 1-12.
  11. J. Pei, J. Han and R. Mao, An efficient algorithm for mining frequent closed itemsets, In proc. 2000 ACM-SIGMOD Int. Workshop Data Mining and Knowledge Discoovery (DMKD 00), 11-20.
  12. J. Pesi. J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M. C. Hsu, Prefix projected pattern growth, In JCDE 01, 215-224.
  13. K. V. Miital, Optimization methods in operation research and system analysis, New Age International, (1996).
  14. K. Wing, S. Zhou and S. C. Liew, Building hierarchical classifiers using class proximity, In VLDB'99, 363-374.
  15. M. Zaki, Generating non-redundant association rules, In KDD'00, 34-43.
  16. Nele Dexters, Approximating the probability of an itemset being frequent, University of Antwerp, Middelheimlaan, Antwerp, Belgium, (2020).
  17. R. Srikant and R. Agrwal, Mining sequential patterns : Generalizations and performance improvements, In EDBT'96, 3-17.
  18. Sunil Joshi, R. S. Jadon and R. C. Jain, An implementation of Frequent pattern mining algorithm using dynamic function, International Journal of Computer Applications, 9(9)(2010), 37-41.
  19. Srivastan Laxman, Prasad Naldrug, Raja Sripada and Ramarathnam Venkatesan, Connections between mining frequent itemsets and learning generative models, Seventh IEEE International Conference on Data Mining (ICDM 2007), (2007), https://doi.org/10.1109/ICDM.2007.83.
  20. Thomas Bernecker, Hans-Kriegel, Matthias Renz and Florian Verhein, Probabilistic frequent itemset mining uncertain databases, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July 1, (2009), https://doi.org/10.1145/1557019.1557039.

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