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Abstract

There is a real increase in the generation of data from different sources. Data mining is a useful method to elicit valuable information. Association rule mining can assist in finding patterns and trends in big data. Also, fuzzy logic plays a main role as an assistance technique in handling big data issues. This review paper present recent literature on the hybridization of association rule mining or other data mining techniques such as classification and clustering and fuzzy logic techniques in big data. Whereas a hybrid model of association rule and fuzzy logic is suggested to get valuable knowledge for big data applications at good accuracy and less time, with the aid of distributed frameworks for big data handling (Hadoop, Spark,and MapReduce). Different techniques and algorithms were used in these works and evaluated according to accuracy, sensitivity, recall, and run time with various results such as sensitivity = 80%, specificity = 86% ,and F-measure = 2.5, or achieving high accuracy and shorter runtime compared to other methods and 98.5 accuraccy of fitness function in pruning redundant rules . At the end of the paper, we present the most prominent and widely used techniques that assist in providing useful and valuable knowledge in different domains from huge, unstructured, and even heterogeneous data. The paper will be beneficial to researches who are interested in the field of mining big data.

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