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The term "Big Data" is a buzzword which describes new technologies that manipulate very large data sets which are massively generated by heterogonous sources. This new term encourages data scientists to extend their work and modify their techniques to overcome the new challenges come with big data concepts. Granular computing has emerged as a new rapidly growing information processing paradigm inside the community of Computational Intelligence. Theories of Fuzzy sets and Rough sets theory are considered powerful examples of granular computing that can be applied to data mining techniques to extract nontrivial knowledge from huge data. The aim of this paper is to introduce a data mining approach for big data based on integrating fuzzy sets and rough sets theories. The proposed approach provides a novel granular data mining for big data that allow extracting useful knowledge and rules from huge data to enhance the decision making process. The proposed approach has been applied on different types of datasets. The experimental results show that our proposed approach is more efficient and robust when dealing with very big datasets and obtained consistent classification rules with classification accuracy 100%.

A Novel Data Mining Approach for Big Data Based on Rough Sets and Fuzzy Logic