Mining fuzzy association rules for classification problems

Authors:
Yi-Chung Hu;Ruey-Shun Chen;Gwo-Hshiung Tzeng
Affiliations:
Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Institute of Management of Technology, National Chiao Tung University, Hsinchu 300, Taiwan, ROC
Venue:
Computers and Industrial Engineering
Year:
2002

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Abstract

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.