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Re-mining Positive and Negative Association Mining Results

  • Ayhan Demiriz
  • Gurdal Ertek
  • Tankut Atan
  • Ufuk Kula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)

Abstract

Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue.

Keywords

Association Rule Frequent Itemsets Association Mining Transaction Data Item Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Building an association rules framework to improve product assortment decisions. Data Min. Knowl. Discov. 8(1), 7–23 (2004)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) SIGMOD Conference, pp. 207–216. ACM Press, New York (1993)Google Scholar
  3. 3.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) SIGMOD Conference, pp. 1–12. ACM Press, New York (1996)Google Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  5. 5.
    Korn, F., Labrinidis, A., Kotidis, Y., Faloutsos, C.: Quantifiable data mining using ratio rules. VLDB J. 8(3-4), 254–266 (2000)CrossRefGoogle Scholar
  6. 6.
    Angiulli, F., Ianni, G., Palopoli, L.: On the complexity of inducing categorical and quantitative association rules. Theoretical Computer Science 314(1-2), 217–249 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Ertek, G., Demiriz, A.: A framework for visualizing association mining results. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 593–602. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press, Menlo Park (1996)Google Scholar
  9. 9.
    Savasere, A., Omiecinski, E., Navathe, S.: Mining for strong negative associations in a large database of customer transactions. In: Proceedings of the 14th International Conference on Data Engineering, pp. 494–502 (1998)Google Scholar
  10. 10.
    Tan, P.N., Kumar, V., Kuno, H.: Using sas for mining indirect associations in data. In: Western Users of SAS Software Conference (2001)Google Scholar
  11. 11.
    Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. J. Intell. Inf. Syst. 20(3), 255–283 (2003)CrossRefGoogle Scholar
  12. 12.
    Yao, Y., Zhao, Y., Maguire, R.B.: Explanation-oriented association mining using a combination of unsupervised and supervised learning algorithms. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 527–531. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Yao, Y., Zhao, Y.: Explanation-oriented data mining. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining. Idea Group Inc., USA (2005)Google Scholar
  14. 14.
    Antonie, M.L., Zaïane, O.R.: An associative classifier based on positive and negative rules. In: Das, G., Liu, B., Yu, P.S. (eds.) DMKD, pp. 64–69. ACM, New York (2004)CrossRefGoogle Scholar
  15. 15.
    Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp. 13–24. ACM, New York (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ayhan Demiriz
    • 1
  • Gurdal Ertek
    • 2
  • Tankut Atan
    • 3
  • Ufuk Kula
    • 1
  1. 1.Sakarya UniversitySakaryaTurkey
  2. 2.Sabanci UniversityIstanbulTurkey
  3. 3.Isik UniversityIstanbulTurkey

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