Implementation of Data Mining Technique for Determining K-Most Demanding Products

Authors

  • Advait Pundlik  BE Students, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India
  • Vaibhav Hood  BE Students, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India
  • Pawan Satpute  BE Students, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India
  • Utkarsh Mandade  BE Students, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India
  • Ankita Tripathi  BE Students, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India
  • Prof. Kapil Hande  Assistant Professor, Department of Computer science and Engineering, Priyadarshini Bhagwati College of Engineering, Nagpur, Maharashtra, India

Keywords:

Algorithm for Knowledge Management, Data Mining, Decision Support, Performance Evaluation of Algorithm

Abstract

This paper formulates an issue for production design as k-most demanding products (k-MDP). Given an arrangement of clients demanding a specific kind of products with numerous traits, an arrangement of existing products of the sort, an arrangement of applicant products that organization can offer, and a positive whole number k, it causes the organization to choose k products from the hopeful products to such an extent that the normal number of the aggregate clients for the k products is augmented. One avaricious algorithm is utilized to discover surmised answer for the issue. Endeavour is likewise made to locate the ideal arrangement of the issue by evaluating the normal number for the aggregate clients of an arrangement of k competitor products for diminishing the pursuit space of the ideal arrangement.

References

  1. Chen-Yi Lin, Jia-Ling Koh, and Arbee L.P. Chen, "Determining k-Most Demanding Products with Maximum Expected Number of Total Customers", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.Harlow, England: Addison-Wesley, 2016.
  2. J. Kleinberg, C. Papadimitriou, and P. Raghavan, "A Microeconomic View of Data Mining",Data Mining and Knowledge Discovery, vol. 2, no. 4, pp. 311-322, 1998.
  3. E. Achtert, C. Bohm, P. Kroger, P. Kunath, A. Pryakhin, and M. Renz, "Efficient Reverse k-Nearest Neighbor Search in Arbitrary Metric Spaces",Proc. 25th ACM SIGMOD Intl Conf. Management of Data, pp. 515-526, 2006.
  4. S. Borzsonyi, D. Kossmann, and K. Stocker, "The Skyline Operator", Proc. 17th Intl Conf. Data Eng., pp. 421-430, 2001.
  5. Y. Tao, D. Papadias, and X. Lian,"Reverse kNN Search in Arbitrary Dimensionality", Proc. 30th Intl Conf. Very Large Data Bases, pp. 744-755, 2004.
  6. W. Wu, F. Yang, C.Y. Chan, and K.L. Tan,"FINCH: Evaluating Reverse k-Nearest-Neighbor Queries on Location Data", Proc. 34th Intl Conf. Very Large Data Bases, pp. 1056-1067, 2008.
  7. E. Dellis and B. Seeger, and K.L. Tan,"Efficient Computation of Reverse Skyline Queries", Proc. 33rd Intl Conf. Very Large Data Bases, pp. 291- 302, 2007.
  8. X. Lian and L. Chen, and K.L. Tan,"Monochromatic and Bichromatic Reverse Skyline Search over Uncertain Databases",Proc. 27th ACM SIGMOD Intl Conf. Management of Data, pp. 213-226, 2008.
  9. C. Li, B.C. Ooi, A.K.H. Tung, and S. Wang,"DADA: A Data Cube for Dominant Relationship Analysis", Proc. 25th ACM SIGMOD Intl Conf. Management of Data, pp. 659-670, 2006.
  10. Q. Wan, R.C.-W. Wong, I.F. Ilyas, M.T. Ozsu, and Y. Peng,"Creating Competitive Products", Proc. 35th Intl Conf. Very Large Data Bases, pp. 898-909, 2009.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 4, March-April-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Advait Pundlik, Vaibhav Hood, Pawan Satpute, Utkarsh Mandade, Ankita Tripathi, Prof. Kapil Hande, " Implementation of Data Mining Technique for Determining K-Most Demanding Products" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.76-81, March-April-2018.