Currently, big data and its applications have become emergent topics. To deal with the uncertainty in data sets, fuzzy system-based models were explored and stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked, or even the invoked rules with missing values in classical fuzzy inference can also appear in big data environment, and therefore, no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this chapter, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.
CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems