Abstract

A number of techniques have been developed to turn data into useful knowledge. Most of the algorithms in data mining find association rules among transactions using binary values and at single concept level. However it will be more exciting to discover hierarchical association rules for decision makers. In this work we have integrated association rule mining with fuzzy set theory and hierarchy. We have proposed an algorithm to discover hierarchical fuzzy association rules. We have used different minimum support and membership functions at each level of hierarchy. We have also used a predefined taxonomy for multilevel of hierarchy.

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