skip to main content
10.1145/3192975.3192987acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccaeConference Proceedingsconference-collections
research-article

Mining Utility Association Rules

Authors Info & Claims
Published:24 February 2018Publication History

ABSTRACT

Mining high utility itemset is to find the itemsets that can bring higher profits to the company, which considers both of the profits and purchased quantities for the items. However, from the high utility itemsets, we cannot know what products should be recommended to the customer such that the profit can be increased when he/she bought some products. Therefore, we propose the definition of the utility association rules and proposes some approaches for mining utility association rules. According to the utility association rules, the company can clearly understand what products should be recommended to the customers when they purchased some items, such that the company can obtain greater benefits. Because there is no previous research on mining utility association rules, we only evaluate the performances of our proposed approaches for mining utility association rules.

References

  1. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules", In Proceedings of the 20th Very Large Data Bases Conference (VLDB), pp. 487--499, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Erwin, R.P. Gopalan, and N.R. Achuthan, "Efficient mining of high utility itemsets from large datasets." Int. Conf. on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, pp 554--561, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Han, R. Mao, J. Pei and Y. Yin, "Mining frequent patterns without candidate generation: a frequent-pattern tree approach", Int. Conf. on Knowledge Discovery and Data Mining, pp. 53--87, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A.B.M.R. Islam and T.S. Chung, "An Improved Frequent Pattern Tree Based Association Rule Mining Technique", Int. Conf. on Information Science and Applications, pp. 1--8, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  5. C. W. Lin, G. C. Lan and T. P. Hong, "Mining high utility itemsets for transaction deletion in a dynamic database", Intelligent Data Analysis, 19(1), 43--55, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. M. Tseng, B.E. Shie, and Philip S. Yu, "Efficient algorithms for mining high utility itemsets from transactional databases", IEEE Transactions on Knowledge and Data Engineering, 25(8), 1772--1786, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. M. Tseng, C.W. Wu, B.E. Shie, and Philip S. Yu, "UP-Growth: An Efficient Algorithm for High Utility Itemset Mining", Proceedings of the 16th ACM SIGKDD International Conference on Knowledge discovery and data mining. ACM, pp. 253--262, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C.W. Wu, B.E. Shie, V.S. Tseng, P.S. Yu, "Mining Top-K High Utility Itemsets", Proceedings of the 18th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, pp 78--86, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S.J. Yen, C.C. Chen, Y.S. Lee, "A Fast Algorithm for Mining High Utility Itemsets", Lecture Notes in Artificial Intelligence (LNAI), pp. 171--182, May 2011.Google ScholarGoogle Scholar
  10. S.J. Yen, C.K. Wang and L.Y. Ouyang "A Search Space Algorithm for Mining Frequent Patterns", Journal of Information Science and Engineering (JISE): Special issue on Technologies and Applications of Artificial Intelligence, Vol.28, No. 1, pp. 177--191, June 2012.Google ScholarGoogle Scholar

Index Terms

  1. Mining Utility Association Rules

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICCAE 2018: Proceedings of the 2018 10th International Conference on Computer and Automation Engineering
      February 2018
      260 pages
      ISBN:9781450364102
      DOI:10.1145/3192975

      Copyright © 2018 ACM

      © 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 February 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader