Study of a New Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Environment

Jian Xu; Bin Ma

doi:10.4028/www.scientific.net/AMM.635-637.1624

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 635-637Study of a New Parallel K_NN Network Public...

Study of a New Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Environment

Abstract:

A new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop environment is studied in this paper. In the light of distributed storage and parallel processing Characteristics of Hadoop platform, the parallel K_NN classification algorithm in the frame of MapReduce is designed. The classification ability and execution efficiency of proposed scheme is verified and the results show that the parallel K_NN algorithm enhances the network public opinion classification precision and execution efficiently.

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Periodical:

Applied Mechanics and Materials (Volumes 635-637)

Pages:

1624-1627

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.635-637.1624

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Cite this paper

Online since:

September 2014

Authors:

Jian Xu*, Bin Ma

Keywords:

Classification, Hadoop, K_ Nearest Neighbor(K_NN), Network Public Opinion

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* - Corresponding Author

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