‘Fuzzy’ vs ‘Non-Fuzzy’ Classification in Big Data

Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data puts forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today, there is not only a necessity for efficient data mining techniques to process large volume of data but also a need for a means to meet the computational requirements to process such huge volume of data. The objective of this research is to compare fuzzy and non-fuzzy algorithms in classification of big data, and to provide a comparative study between the results of this study and the methods reviewed in the literature. In this paper, we implemented the Fuzzy K-Nearest Neighbor method as a fuzzy technique and the Support Vector Machine as nonfuzzy technique using the map reduce paradigm to process on big data. Results on different data sets show that the proposed Fuzzy K Nearest Neighbor method outperforms a better performance than the Support Vector Machine and the method reviewed in the literature.