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Soft Computing

, Volume 17, Issue 7, pp 1227–1239 | Cite as

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

  • Mojtaba Asadollahpour ChamaziEmail author
  • Behrouz Minaei Bidgoli
  • Mahdi Nasiri
Foundations

Abstract

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.

Keywords

Level-crossing fuzzy association rules Multiple-level IFG mining Cluster-based master–slave technique 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mojtaba Asadollahpour Chamazi
    • 1
    Email author
  • Behrouz Minaei Bidgoli
    • 2
  • Mahdi Nasiri
    • 2
  1. 1.Computer Society of IranTehranIran
  2. 2.Department of Computer EngineeringIran University of Science and TechnologyTehranIran

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