Mining fuzzy association rules in incomplete databases

Mining quantitative association rules is a particular subject of interest in fuzzy set application theory. However, the theory generally applies to a transactional database with no missing values. A predictive algorithm is proposed in this paper in order to extrapolate (interpolate) the unknown values. A fuzzy data mining algorithm is used to discover fuzzy association rules over the extended database with filled predictive values.

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