Discovering Patterns Based on Fuzzy Logic Theory

  • Bobby D. Gerardo
  • Jaewan Lee
  • Su-Chong Joo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3983)


This study investigates the formulation of fuzzy logic as integrated component of the proposed model in data mining in order to classify the dataset prior to the implementation of data mining tools such summarization, association rule discovery, and prediction. The novel contribution of this paper is the fuzzification of the dataset prior to pattern discovery. The model is compared to the classical clustering, regression model, and neural network using the Internet usage database available at the UCI Knowledge Discovery on Databases (KDD) archive. Our test is anchored on parameters like relevant measure, processing performance, discovered rules or patterns and practical use of the findings. The proposed model indicates adequate performance in clustering, higher clustering accuracy and efficient pattern discovery compared with the other models.


Fuzzy Logic Association Rule Fuzzy Cluster Fuzzy Theory Pattern Discovery 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bobby D. Gerardo
    • 1
  • Jaewan Lee
    • 1
  • Su-Chong Joo
    • 2
  1. 1.School of Electronic and Information EngineeringKunsan National UniversityChonbukSouth Korea
  2. 2.School of Electrical Electronic and Information EngineeringWonkwang UniversityChonbukSouth Korea

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