A Fast-Effective Algorithm on A Concise Representation of Top Rated Utility Mining Datasets

Authors

  • Shaik Subani  M.Tech Scholar Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India
  • B. V. N. Krishna Suresh  Assistant Professor Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET207446

Keywords:

Battery Storage, Super capacitors, Renewable Resources, Wind Power, Supervisory Controller, Battery Lifetime

Abstract

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose Trust-reputation, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of Trust-reputation through extensive experiments in comparisons to state-of-the-art approaches.

References

  1. A. Abdul-Rahman, S. Hailes (2000) “Supporting Trust in Virtual Communities,” Proc. 33rd Hawaii International Conference on System Sciences.
  2. G. Akerlof (1970) “The Market for ‘Lemons’: Qualitative Uncertainty and the Market Mechanism,” Quarterly Journal of Economics, 84, pp. 488-500.
  3. R. Axelrod (1984) The Evolution of Cooperation. New York: Basic Books.
  4. P. Bajari, A. Hortacsu (1999) “Winner’s Curse, Reserve Prices and Endogenous entry: Empirical Insights from eBay Auctions,” Stanford Institute for aEconomic Policy Research (SIEPR) Policy paper No. 99-23.
  5. J. H. Barkow, L. Cosmides, J. Tooby (eds.) (1992) The Adapted Mind: Evolutionary Psychology and the Generation of Culture. Oxford: Oxford University Press
  6. L. C. Becker, (1990) Reciprocity. Chicago: University of Chicago Press.
  7. K. Binmore (1997) "Rationality and Backward Induction," Journal of Economic Methodology, 4, pp. 23 -41.
  8. R. Boyd and P. J. Richerson (1989) “The Evolution of Indirect Reciprocity,” Social Networks, 11, pp. 213-236.
  9. C. Castelfranchi, R. Conte, M. Paolucci (1998) “Normative Reputation and the Costs of Compliance,” Journal of Artificial Soci eties and Social Simulations, 1(3).
  10. S. Krishnamoorthy, “Pruning strategies for mining high utility itemsets,” Expert Syst. Appl., vol. 42, no. 5, pp. 2371-2381, 2015.
  11. C. Lin, T. Hong, G. Lan, J. Wong, and W. Lin, “Ef?cient updating of discovered high-utility itemsets for transaction deletion in dynamic databases,” Adv. Eng. Informat., vol. 29, no. 1, pp. 16-27, 2015.
  12. G. Lan, T. Hong, V. S. Tseng, and S. Wang, “Applying the maximum utility measure in high utility sequential pattern mining,” Expert Syst. Appl., vol. 41, no. 11, pp. 5071-5081, 2014.
  13. Y. Liu, W. Liao, and A. Choudhary, “A fast high utility itemsets mining algorithm,” in Proc. Utility-Based Data Mining Workshop, 2005, pp. 90-99.
  14. M. Liu and J. Qu, “Mining high utility itemsets without candidate generation,” in Proc. ACM Int. Conf. Inf. Knowl. Manag., 2012, pp. 55-64.
  15. J. Liu, K. Wang, and B. Fung, “Direct discovery of high utility itemsets without candidate generation,” in Proc. IEEE Int. Conf. Data Mining, 2012, pp. 984-989.
  16. Y. Lin, C. Wu, and V. S. Tseng, “Mining high utility itemsets in big data,” in Proc. Int. Conf. Paci?c-Asia Conf. Knowl. Discovery Data Mining, 2015, pp. 649-661.
  17. Y. Li, J. Yeh, and C. Chang, “Isolated items discarding strategy for discovering high-utility itemsets,” Data Knowl. Eng., vol. 64, no. 1, pp. 198-217, 2008.
  18. J. Pisharath, Y. Liu, B. Ozisikyilmaz, R. Narayanan, W. K. Liao, A. Choudhary, and G. Memik, NU-MineBench version 2.0 dataset and technical report Online]. Available: http://cucis.ece.northwestern.edu/projects/DMS/MineBench.html, 2005.
  19. G. Pyun and U. Yun, “Mining top-k frequent patterns with combination reducing techniques,” Appl. Intell., vol. 41, no. 1, pp. 76-98, 2014.
  20. T. Quang, S. Oyanagi, and K. Yamazaki, “ExMiner: An ef?cient algorithm for mining top-k frequent patterns,” in Proc. Int. Conf. Adv. Data Mining Appl., 2006, pp. 436 - 447.
  21. H. Ryang and U. Yun, “Top-k high utility pattern mining with effective threshold raising Strategies,” Knowl.-Based Syst., vol. 76, pp. 109-126, 2015.
  22. H. Ryang, U. Yun, and K. Ryu, “Discovering high utility itemsets with multiple minimum supports,” Intell. Data Anal., vol. 18, no. 6, pp. 1027-1047, 2014.
  23. B. Shie, H. Hsiao, V. S. Tseng, and P. S. Yu, “Mining high utility mobile sequential patterns in mobile commerce environments,” in Proc. Int. Conf. Database Syst. Adv. Appl. Lecture Notes Comput. Sci., 2011, vol. 6587, pp. 224-238.
  24. P. Tzvetkov, X. Yan, and J. Han, “TSP: Mining top-k closed sequential patterns,” Knowl. Inf. Syst., vol. 7, no. 4, pp. 438-457, 2005.
  25. V. S. Tseng, C. Wu, B. Shie, and P. S. Yu, “UP-Growth: An ef?cient algorithm for high utility itemset mining,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 253-262.
  26. V. S. Tseng, C. Wu, P. Fournier-Viger, and P. S. Yu, “Ef?cient algorithms for mining the concise and lossless representation of high utility itemsets,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 3, pp. 726-739, Mar. 1, 2015.
  27. C. Wu, P. Fournier-Viger, P. S. Yu, and V. S. Tseng, “Ef?cient mining of a concise and lossless representation of high utility itemsets,” in Proc. IEEE Int. Conf. Data Mining, 2011, pp. 824-833.
  28. J. Wang and J. Han, “TFP: An ef?cient algorithm for mining top-k frequent closed itemsets,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 5, pp. 652-663, May 2005.
  29. C. Wu, Y. Lin, P. S. Yu, and V. S. Tseng, “Mining high utility episodes in complex event sequences,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2013, pp. 536-544.
  30. C. Wu, B. Shie, V. S. Tseng, and P. S. Yu, “Mining top-k high utility itemsets,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 78-86.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
Shaik Subani, B. V. N. Krishna Suresh "A Fast-Effective Algorithm on A Concise Representation of Top Rated Utility Mining Datasets" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 4, pp.188-194, July-August-2020. Available at doi : https://doi.org/10.32628/IJSRSET207446