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Big Data Classification using Fuzzy K-Nearest Neighbor

by Malak El Bakry, Soha Safwat, Osman Hegazy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 10
Year of Publication: 2015
Authors: Malak El Bakry, Soha Safwat, Osman Hegazy
10.5120/ijca2015907591

Malak El Bakry, Soha Safwat, Osman Hegazy . Big Data Classification using Fuzzy K-Nearest Neighbor. International Journal of Computer Applications. 132, 10 ( December 2015), 8-13. DOI=10.5120/ijca2015907591

@article{ 10.5120/ijca2015907591,
author = { Malak El Bakry, Soha Safwat, Osman Hegazy },
title = { Big Data Classification using Fuzzy K-Nearest Neighbor },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 10 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number10/23628-2015907591/ },
doi = { 10.5120/ijca2015907591 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:58.872413+05:30
%A Malak El Bakry
%A Soha Safwat
%A Osman Hegazy
%T Big Data Classification using Fuzzy K-Nearest Neighbor
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 10
%P 8-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Because of the massive increase in the size of the data it becomes troublesome 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. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this paper is to classify big data using Fuzzy K-Nearest Neighbor classifier, and to provide a comparative study between the results of the proposed systems and the method reviewed in the literature. In this paper we implemented the Fuzzy K-Nearest Neighbor method using the MapReduce 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 method reviewed in the literature.

References
  1. S Mitha T, MCA, M.Phil, & M.Tech, V. (2013). Application of Big Data in Data Mining. International Journal of Emerging Technology and Advanced Engineering, 3(7), 390-393.
  2. P Anchalia, Prajesh, and Kaushik Roy. The K-Nearest Neighbor Algorithm Using MapReduce Paradigm. Fifth International Conference on Intelligent Systems, Modelling And Simulation. 2014. Web. 15 Oct. 2015.
  3. Koturwar, P., Girase, S., & Mukhopadhyay, D. (2015). A Survey of Classification Techniques in the Area of Big Data.
  4. Pakize, S., & Gandomi, A. (2014). Comparative Study of Classification Algorithms Based On MapReduce Model. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 1(7), 251-254.
  5. Tobin, K., Gleason, S., & Karnowski, T. (n.d.). Adaptation Of The Fuzzy K-Nearest Neighbor Classifier For Manufacturing Automation.
  6. Sharma, C. (2014). Big Data Analytics Using Neural networks.
  7. Río, S., López, V., Benítez, J., & Herrera, F. (2015). A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules. International Journal of Computational Intelligence Systems, 422-437.
  8. Nasullah Khalid Alham, Maozhen Li, Yang Liu, and Suhel Hammoud, (2011). a MapReduce-based distributed SVM algorithm for automatic image annotation
  9. Xu, K., Wen, C., Yuan, Q., He, X., & Tie, J. (2014). A MapReduce based Parallel SVM for Email Classification. Journal of Networks JNW.
  10. Zhiqiang Liu; Hongyan Li ; Gaoshan Miao.MapReduce-based Backpropagation Neural Network over large scale mobile data
  11. Changlong Li1, Xuehai Zhou1, Kun Lu1. Implementation of Artificial Neural Networks in MapReduce Optimization.
  12. Bhagattjee, B. (2014). Emergence and Taxonomy of Big Data as a Service.
  13. Wu, X., Zhu, X., Wu, G., & Ding, W. (n.d.). Data mining with big data. IEEE Trans. Knowl. Data Eng. IEEE Transactions on Knowledge and Data Engineering, 97-107.
  14. Keller, J.M., Gray, M.R., and Given, J.A., (1985). A Fuzzy K-Nearest Neighbor Algorithm. IEEE Trans. Syst., Man, Cybern., Syst., 15 (4), 580-585.
  15. MRPR: A MapReduce solution for prototype reduction in big data classification Neurocomputing, Vol. 150 (February 2015), pp. 331-345, by Isaac Triguero, Daniel Peralta, Jaume Bacardit, Salvador García, Francisco Herrera
  16. Río, S., López, V., Benítez, J., & Herrera, F. (2015). A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules. International Journal of Computational Intelligence Systems, 422-437.
Index Terms

Computer Science
Information Sciences

Keywords

Big data Classification Fuzzy k-nearest neighbor Fuzzy logic Hadoop MapReduce

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