Abstract
Defining data with fuzziness made the knowledge discovery process easy and secure to data in data mining. The fuzzy data bases may have linguistic variables. In this paper, fuzzy conditional inference and reasoning are studied for generalized fuzzy data mining. Generalized fuzzy data mining and reasoning is studied with two membership functions “Belief” and “Disbelief”. The fuzzy logic with two membership functions will give more evidence than single membership function. The fuzzy certainty factor is studied as difference between these functions and made it as single membership function. The fuzzy data mining methods are studied. The generalized data mining is studied with different fuzzy conditional inferences. The business intelligence is given as an example.
Highlights
Zadeh [1] defined fuzzy set with single membership function
Fuzzy conditional inference and reasoning are studied for generalized fuzzy data mining
Generalized fuzzy data mining and reasoning is studied with two membership functions “Belief” and “Disbelief”
Summary
Zadeh [1] defined fuzzy set with single membership function. The main objective of fuzzy data mining is knowledge discovery process. The reasoning may be considered as one of the data mining technique during knowledge discovery process. The generalized fuzzy data mining with two membership function will give more evidence. The fuzzy data mining and fuzzy reasoning made the knowledge discovery process easy through the overall observation and reasoning. The two membership functions shall be made as single fuzzy mem-. S. Reddy bership function with fuzzy certainty factor. The fuzzy certainty factor will give single membership as difference between two membership functions. Fuzzy conditional inference and reasoning are studied. The fuzzy certainty factor is studied as single membership function. The generalized fuzzy data mining and reasoning are studied
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