Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model

  • Shu-Hsien Liao
  • Yin-Ju Chen
  • Pei-Hui Chu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6443)


Of the consumers who often patronize retail stores, 87 of 100 respondents visited a convenience store in the past three months. The superstore/hypermarket and the supermarket came in second and third, respectively. This demonstrates that retail channels are essential to the day-to-day life of the common populace. With the social and economic evolution, not only have product sales and shopping habits changed, but the current marketing concepts have also changed from being product-oriented to being consumer-oriented. In this research, we first provide new algorithms modified from the Apriori algorithm. The new approach can be applied in finding association rules, which can handle an uncertainty, combined with the rough set theory, and then to find the influence degree of the consumer preferences variables for the marketing decision-makers used.


Data mining Rough set Association rule Retailing industry Brand trust 


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  1. 1.
    Lee, J.-S., Back, K.-J.: Attendee-based brand equity. Tourism Management 29, 331–344 (2008)CrossRefGoogle Scholar
  2. 2.
    Kent, R.J., Kellaris, J.: Competitive interference effects in memory for advertising: are familiar brands exempt? J. Mark Commun. 7, 59–69 (2001)CrossRefGoogle Scholar
  3. 3.
    Walczak, B., Massart, D.L.: Rough sets theory. Chemometrics &Intelligent Laboratory Systems 47,1, 1–16 (1999)Google Scholar
  4. 4.
    Greco, S., Inuiguchi, M., Slowinski, R.: Fuzzy rough sets and multiple-premise gradual decision rules. International Journal of Approximate Reasoning 41, 179–211 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Liu, M., Chen, D., Wu, C., Li, H.: Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems. Computers and Mathematics with Applications 51, 1507–1518 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Lian, W., Cheung, D.W., Yiu, S.M.: An efficient algorithm for finding dense regions for mining quantitative association rules. Computers and Mathematics with Applications 50, 471–490 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lee, T.-S., Chiu, C.-C., Chou, Y.-C., Lu, C.-J.: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis 50(4), 1113–1130 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM 39, 27–34 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shu-Hsien Liao
    • 1
  • Yin-Ju Chen
    • 2
  • Pei-Hui Chu
    • 2
  1. 1.Department of Management Sciences and Decision MakingTamkang UniversityTaipeiTaiwan, ROC
  2. 2.Department of Management SciencesTamkang UniversityTaipeiTaiwan, ROC

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