Discovering patterns based on fuzzy logic theory

Authors:
Bobby D. Gerardo;Jaewan Lee;Su-Chong Joo
Affiliations:
School of Electronic and Information Engineering, Kunsan National University, Chonbuk, South Korea;School of Electronic and Information Engineering, Kunsan National University, Chonbuk, South Korea;School of Electrical Electronic and Information Engineering, Wonkwang University, Chonbuk, South Korea
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2006

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Abstract

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.