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CLC number: TP391

On-line Access: 2013-06-04

Received: 2012-10-24

Revision Accepted: 2013-04-01

Crosschecked: 2013-05-13

Cited: 4

Clicked: 2861

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.6 P.407-416

http://doi.org/10.1631/jzus.C1200303


Accelerated k-nearest neighbors algorithm based on principal component analysis for text categorization


Author(s):  Min Du, Xing-shu Chen

Affiliation(s):  School of Computer Science, Sichuan University, Chengdu 610065, China

Corresponding email(s):   doingscu@gmail.com, chenxsh@scu.edu.cn

Key Words:  k-nearest neighbors (kNN), Text categorization, Accelerating strategy, Principal component analysis (PCA)


Min Du, Xing-shu Chen. Accelerated k-nearest neighbors algorithm based on principal component analysis for text categorization[J]. Journal of Zhejiang University Science C, 2013, 14(6): 407-416.

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author="Min Du, Xing-shu Chen",
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%T Accelerated k-nearest neighbors algorithm based on principal component analysis for text categorization
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%A Xing-shu Chen
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T1 - Accelerated k-nearest neighbors algorithm based on principal component analysis for text categorization
A1 - Min Du
A1 - Xing-shu Chen
J0 - Journal of Zhejiang University Science C
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EP - 416
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200303


Abstract: 
text categorization is a significant technique to manage the surging text data on the Internet. The k-nearest neighbors (kNN) algorithm is an effective, but not efficient, classification model for text categorization. In this paper, we propose an effective strategy to accelerate the standard kNN, based on a simple principle: usually, near points in space are also near when they are projected into a direction, which means that distant points in the projection direction are also distant in the original space. Using the proposed strategy, most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point, which greatly decreases the computation cost. Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN, with little degradation in accuracy. Specifically, it is superior in applications that have large and high-dimensional datasets.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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