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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 12 | Page No.: 2208-2212
DOI: 10.3923/jas.2013.2208.2212
 
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A Fast Classification Algorithm for Big Data Based on KNN
Kun Niu, Fang Zhao and Shubo Zhang

Abstract:
As massive data acquisition and storage becomes increasing affordable, a wide variety of researchers are employing methods to engage in sophisticated data mining. This study focuses on fast classification for big data based on a traditional classification method KNN (K-Nearest Neighbor). We reform the standard KNN algorithm and present a new algorithm named NFC (Neighbor Filter Classification). The NFC algorithm firstly computes the class distribution in each attribute of original dataset and sorts attributes by classification contribution. Secondly, NFC gets the model of the KNN result on training set to estimate the finite scope of the k-nearest neighbor. Then NFC uses test set to get the proper parameters and updates model regularly to make it efficient. Experimental results show the excellent ability of classification and low computation cost of NFC.
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How to cite this article:

Kun Niu, Fang Zhao and Shubo Zhang, 2013. A Fast Classification Algorithm for Big Data Based on KNN. Journal of Applied Sciences, 13: 2208-2212.

DOI: 10.3923/jas.2013.2208.2212

URL: https://scialert.net/abstract/?doi=jas.2013.2208.2212

COMMENTS
02 November, 2014
Sriram Kankatala:
Hi authors,

I have gone through you paper.You did a great and innovative approach in data mining.I am a masters student working on my masters thesis on classification of data using CUDA. I am very happy to see that you developed an approach which i am also looking, for as my reference.I am very happy if you help me.You developed your NFC on java,i would like to see this code. Because i am going to implement on CUDA programming.I am very happy if you reply me.Can you please provide your personal mail id ? If possible can you send more description about your paper.




Thanks and regards,

SRIRAM KANKATALA

M.Sc in Telecommunication Systems,

School of Computing,

Blekinge Institute of Technology,

37179,Karlskrona,Sweden
 
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