Big Data Analytics Using fuzzy logic approach

Abstract

In the era of big data, we are facing with an immense volume and high velocity of data with complex structures. Data can be produced by online and offline transactions, social networks, sensors and through our daily life activities. A proper processing of big data can result in informative, intelligent and relevant decision making completed in various areas, such as medical and healthcare, business, management and government. Fuzzy sets have been employed for big data analyzing due to their abilities to represent and quantify aspects of uncertainty. In this thesis, we have implemented and analyzed big dataset using a fuzzy logic where four rule groups are used and their results have been analyzed and compared. Results showed that the number of rules affects the false negative and the false positive rates when final decisions are compared to those of experts.