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Research Article

A Fast Classification Algorithm for Big Data Based on KNN

Kun Niu, Fang Zhao and Shubo Zhang
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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


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