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)


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.


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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© 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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