A Text Categorization Method Based on Improved k-means and BP Neural Network

DOI

10.2991/icsecs-13.2013.51How to use a DOI?

Keywords

BP neural network; text categorization; k-means

Abstract

K-means is a widely used cluster algorithm. It is widely used in text categorization as an unsupervised method. However, it could be easily affected by some isolated observations. BP neural network is usually used for text categorization because it’s superiority in handling non-linear problem. However, sometimes it could not achieve high performance. Based on the combination of these two algorithms, we propose a new text categorization algorithm. We first improve k-means clustering algorithm. After that, we use it to cluster vectors in our vector space model. And then, BP neural network is used to categorize the preprocessed vectors. The experiments show that our algorithm could achieve a high performance than the traditional BP neural network text categorization method.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rongze Xia
AU  - Yan Jia
AU  - Hu Li
PY  - 2013/09
DA  - 2013/09
TI  - A Text Categorization Method Based on Improved k-means and BP Neural Network
BT  - Proceedings of the 2013 International Conference on Software Engineering and Computer Science
PB  - Atlantis Press
SP  - 237
EP  - 240
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsecs-13.2013.51
DO  - 10.2991/icsecs-13.2013.51
ID  - Xia2013/09
ER  -