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K-Nearest Neighbor Algorithm Optimization in Text Categorization

IOP conference series. Earth and environmental science, 2018-01, Vol.108 (5), p.52074 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1755-1307 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/108/5/052074

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  • Title:
    K-Nearest Neighbor Algorithm Optimization in Text Categorization
  • Author: Chen, Shufeng
  • Subjects: Algorithms ; Classification ; Data mining ; K nearest neighbour classification tree analysis ; Optimization ; Pattern recognition
  • Is Part Of: IOP conference series. Earth and environmental science, 2018-01, Vol.108 (5), p.52074
  • Description: K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods of data mining. It has been widely used in classification, regression and pattern recognition. The traditional KNN method has some shortcomings such as large amount of sample computation and strong dependence on the sample library capacity. In this paper, a method of representative sample optimization based on CURE algorithm is proposed. On the basis of this, presenting a quick algorithm QKNN (Quick k-nearest neighbor) to find the nearest k neighbor samples, which greatly reduces the similarity calculation. The experimental results show that this algorithm can effectively reduce the number of samples and speed up the search for the k nearest neighbor samples to improve the performance of the algorithm.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1755-1307
    EISSN: 1755-1315
    DOI: 10.1088/1755-1315/108/5/052074
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    Alma/SFX Local Collection
    ProQuest Central
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