Enrichment on High Utility Sub Graph Mining on Transactional Database

Authors

  • Akshatha H M Amrita School of Arts and Sciences, Mysuru,Amrita Vishwa Vidyapeetham, India Author

DOI:

https://doi.org/10.61841/gm99sj94

Keywords:

Data mining,, High utility,, Graph mining, Frequent mining.

Abstract

Enrichment on High utility item set that produces a great deal of benefit for the seller. High utility item set mining is an effective technique for dynamic huge data. Fundamentally the database that contains products with amounts and mining ability is limited to transaction data consist of items. Graph mining is a non- trival graph structure from a confounded system. In the proposed technique that we are going to change over the transaction database into sub graph structure. By utilizing high utility sub graph mining calculation and it produces yield as a graph structure.

 

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References

1. Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.

2. Liu, D., Yan, Z., Ding, W., & Atiquzzaman, M. (2019). A Survey on Secure Data Analytics in Edge Computing. IEEE Internet of Things Journal.

3. Truong, T., Duong, H., Le, B., & Fournier-Viger, P. (2018). Efficient vertical mining of high average- utility item sets based on novel upper-bounds. IEEE Transactions on Knowledge and Data Engineering, 31(2), 301-314.

4. Jena, B. S., Khan, C., & Sunderraman, R. (2019). High performance frequent subgraph mining on transaction datasets: A survey and performance comparison. Big Data Mining and Analytics, 2(3), 159- 180.

5. Lin, J. C. W., Ren, S., Fournier-Viger, P., Pan, J. S., & Hong, T. P.(2018). Efficiently updating the discovered high average-utility item sets with transaction insertion. Engineering Applications of Artificial Intelligence, 72, 136-149.

6. Krishnamoorthy, S. (2019). Mining top-k high utility item sets with effective threshold raising strategies. Expert Systems with Applications, 117, 148-165.

7. Suresh. K and Pattabiraman. V, “An improved utility itemsets mining with respect to positive and negative values using mathematical model”, International Journal of Pure and Applied Mathematics, Volume-101 No-5, Page No-763772, 2015.

8. Anusha, Koyi & C, Yashaswini & Sankar, Manishankar. (2016). Segmentation of Retail Mobile Market Using HMS Algorithm. International Journal of Electrical and Computer Engineering 6(4):1818, August 2016.

9. Sindhushree B, Manishankar S, Dhanushya B P, Cloud Based Healthcare Framework for Criticality Level Analysis, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8, Issue-1, May 2019

10. P. Rakshith, S. Manishankar and P. Sushmitha, "Enterprise data analytics and processing with an integrated hadoop and R platforms," 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, 2017, pp. 1-5.

11. R. Baker, A. Corbett, and K. Koedinger, “Detecting student misuse of intelligent tutoring systems,” in Proc. Int. Conf. Intell. Tutoring Syst., Alagoas, Brazil, 2004, pp. 531–540.

12. R. Baker, “Modeling and understanding students’ off-task behavior in intelligent tutoring systems,” in Proc. Conf. Hum. Factors Comput. Syst., San Jose, CA, 2007, pp. 1059–1068.

13. R. Baker, J. E. Beck, B. Berendt, A. Kroner, E. Menasalvas, and S. Weibelzahl, “Track on educational data mining,” presented at the 11th Int. Conf. User Model. Workshop Data Mining User Model, Corfu, Greece, 2007.

14. Suresh, K., and Devika Mohan. "Development of High Utility Itemsets In Streaming Database." TEST Engineering & Management 82 (2020): 13052-13056.

15. Suresh, K., and V. Pattabiraman. "Reduction Of Large Database And Identifying Frequent Patterns Using Enhanced High Utility Mining." International Journal of Pure and Applied Mathematics 109.5 (2016): 161-169.

16. R. Baker and K. Yacef, “The state of educational data mining in 2009: A review and future visions,” J. Educ. Data Mining, vol. 1, no. 1, pp. 3–17, 2009.

17. R. Baker, “Data mining for education,” in International Encyclopedia of Education, B. McGaw, P. Peterson, and E. Baker, Eds., 3rd ed. Oxford, U.K.: Elsevier, 2010.

18. R. Baker, A. Merceron, and P. I. Pavilk, presented at the 3rd Int. Conf. Educ. Data Mining, Pittsburgh, PA, 2010.

19. H. Ba-Omar, I. Petrounias, and F. Anwar, “A framework for using web usage mining for personalise e- learning,” in Proc. Int. Conf. Adv. Learn. Technol., Niigata, Japan, 2007, pp. 937–938.

20. D. Barker-Plummer, R. Cox, and R. Dale, “Dimensions of difficulty in translating natural language into fist order logic,” in Proc. Int. Conf. Educ. Data Mining, Cordoba, Spain, 2009, pp. 220–228.

21. T. Barnes, “The q-matrix method: Mining student response data for knowledge,” in Proc. AAAI Workshop Educ. Data Mining, Pittsburgh, PA, 2005, pp. 1–8.

22. T. Barnes and J. Stamper, “Toward automatic hint generation for logic proof tutoring using historical student data,” in Int. Conf. Intell. Tutoring Syst., Montreal, QC, Canada, 2008, pp. 373–382.

23. T. Barnes, M. Desmarais, C. Romero, and S. Ventura, presented at the 2nd Int. Conf. Educ. Data Mining, Cordoba, Spain, 2009.

24. C. B. Baruque, M. A. Amaral, A. Barcellos, J. C. Da Silva Freitas, and C. J. Longo, “Analysing users’ access logs in Moodle to improve e learning,” in Proc. Euro Amer. Conf. Telematics Inf. Syst., Faro, Portugal, 2007, pp.1–4.

25. C. R. Beal and P. R. Cohen, “Temporal data mining for educational applications,” in Proc. 10th Pacific Rim Int. Conf. Artif. Intell.: Trends Artif. Intell., Hanoi, Vietnam, 2008, pp. 66–77.

26. J. E. Beck, presented at the 5th Int. Conf. Intell. Tutoring Syst. (ITS) Workshop Applying Mach. Learning to ITS Design/Construction, Montreal, QC, Canada, 2000.

27. J. E. Beck, and B. P. Woolf, “High-level student modeling with machine learning,” in Proc. 5th Int. Conf. Intell. Tutoring Syst., Alagoas, Brazil, 2000, pp. 584–593.

28. H. Mannila, H. Toivonen, and A. I. Verkamo, Discovery of frequent episodes in event sequences, Data Min. Know. Disc., vol. 1, no. 3, pp. 259–289, 1997.

29. R. Agrawal and R. Srikant, Mining sequential patterns, in Proc. Eleventh Int. Conf. Data Engineering, Washington, DC, USA, 1995, pp. 3–14.

30. T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa, Efficient substructure discovery from large semi-structured data, in Proc. 2002 SIAM Int. Conf. Data Mining, Arlington, VA, USA, 2002.

31. M. J. Zaki, efficiently mining frequent trees in a forest, in Proc. Eighth ACM SIGKDD Int. Conf. Knowledge

32. Discovery and Data Mining, 2002, pp. 71–80.

33. R. C. Agarwal, C. C. Aggarwal, and V. V. V. Prasad, A tree projection algorithm for generation of frequent item sets, J. Parall. Distrib. Comput., vol. 61, no. 3, pp. 350–371,2001.

34. M. Kuramochi and G. Karypis, Frequent subgraph discovery, in Proc. IEEE Int. Conf. Data Mining, San Jose,CA, USA, 2001, pp. 313–320.

35. [34] X. Yan, Mining, indexing and similarity search in large graph data sets, PhD dissertation, University of Illinois at Urbana-Champaign, IL, USA, 2006.

36. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms. Boston, MA, USA: MIT Press and McGraw-Hill, 2001.

37. Savasere, E. Omiecinski, and S. B. Navathe, An efficient algorithm for mining association rules in large databases, in Proc. 21th Int. Conf. Very Large Data Bases, San Francisco, CA, USA, 1995, pp. 432–444

38. Wang, W. Wang, J. Pei, Y. T. Zhu, and B. L. Shi, Scalable mining of large disk-based graph databases, in Proc. Tenth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2004, pp. 316–325.

39. J. M. Wang, W. Hsu, M. L. Lee, and C. Sheng, A partition-based approach to graph mining, in Proc. 22nd Int. Conf. Data Engineering, Atlanta, GA, USA, 2006, p. 74.

40. S. N. Nguyen, M. E. Orlowska, and X. Li, Graph mining based on a data partitioning approach, in Proc. Nineteenth Conf. Australasian Database, Darlinghurst, Australia, 2007, pp. 31–37.

41. S. N. Nguyen and M. E. Orlowska, Improvements in the data partitioning approach for frequent itemsets mining, in Knowledge Discovery in Databases: PKDD 2005, A. M. Jorge, L. Torgo, P. Brazdil, R. Camacho, and J. Gama, eds. Springer, 2005, pp. 625–633.

42. S. Chakravarthy, R. Beera, and R. Balachandran, DB- Subdue: Database approach to graph mining, in Advances in Knowledge Discovery and Data Mining, H. Dai, R. Srikant, and C. Zhang, eds. Springer, 2004, pp. 341–350.

43. J. Cook and L. B. Holder, Graph-based data mining, IEEE Intell. Syst. Their Appl., vol. 15, no. 2, pp. 3241, 2000.

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Published

30.06.2020

How to Cite

M, A. H. (2020). Enrichment on High Utility Sub Graph Mining on Transactional Database. International Journal of Psychosocial Rehabilitation, 24(6), 5730-5739. https://doi.org/10.61841/gm99sj94