الفهرس 
A comparative study to enhance big data classification using fuzzy technique
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Abstract
Due to the huge increase in size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today, it is not only a necessity for efficient data mining techniques to process large volume of data but also a need for means to meet the computational requirements to process such huge volume of data. The objective of this research is to implement classification techniques using the map reduce framework using fuzzy and crisp methods. Further it provides a comparative study between the results of the proposed systems and the methods reviewed in the literature. In this thesis we implemented the fuzzy K-Nearest Neighbor method as a fuzzy technique. It will be compared to both the support vector machine and the k-nearest neighbor as non-fuzzy techniques using the map reduce paradigm to process on big data. We proposed an integrated system using the support vector machine with the fuzzy soft label and gaussian fuzzy membership. Results show that fuzzy k-nearest neighbor classifier gives higher accuracy but it takes a lot of time in classification compared to the other techniques. But results on different data sets show the proposed method that used fuzzy logic in the reducer function gives higher accuracy and lower time than the crisp proposed methods and the methods reviewed in the literature