Generalized fuzzy data mining for incomplete information

Defining data inherently with fuzziness reduce the complexity of data mining during knowledge discovery process. The mining with fuzzy database will provide security because the original data need not be disclosed The fuzzy logic with two membership functions will give more evidence than the Zadeh single membership function. In this paper, the fuzzy data mining methods are discussed. The two fold fuzzy set with two membership functions is studied with "Belief" and ""Disbelief". The fuzzy certainty factor (FCF) is difference of the two membership functions to eliminate conflict, The FCF and gives single fuzzy membership function. The fuzzy risk set is defined with fuzzy certainty factor for decision making. The fuzzy MapReducing with functional dependency is studied for association rules. The Generalized fuzzy reasoning is studied for data mining. The data mining with fuzzy risk set is studied to take the decisions. The business intelligence is given as an example.