Author(s): Jay Kumar Jain, Anshu Shrivastava and Dipti Chauhan

Abstract: Due to the enormous increase in the size of the data, it becomes wearisome to perform effective 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. The application of data mining has yielded positive results in the information gathering of big data. However, the current growth of technology had made it very difficult for software developers and data analysts to gather information on Big Data using existing data mining algorithms such as K-Nearest Neighbour (KNN), K-means, Support Vector Machines (SVM), Apriori, Page-rank, and AdaBoost. In this paper, we implemented the Fuzzy K-Nearest Neighbor method using the Map-Reduce paradigm to process big data. Results on different data sets show that the proposed Fuzzy K-Nearest Neighbor method outperforms a better performance than the method reviewed in the literature.

DOI: 10.33545/27076571.2022.v3.i2a.48

Pages: 01-06 | Views: 2133 | Downloads: 1343

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