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Proceedings Paper

A data mining algorithm based on the rough sets theory and BP neural network
Author(s): Weijin Jiang; Yusheng Xu; Yuhui Xu
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Paper Abstract

As both rough sets theory and neural network in data mining have special advantages and exiting problems, this paper presented a combined algorithm based rough sets theory and BP neural network. This algorithm deducts data from data warehouse by using rough sets' deduct function, and then moves the deducted data to the BP neural network as training data. By data deduct, the expression of training will become clearer, and the scale of neural network can be simplified. At the same time, neural network can easy up rough set's sensitivity for noise data. This paper presents a cost function to express the relationship between the amount of training data and the precision of neural network, and to supply a standard for the change from rough set deduct to neural network training.

Paper Details

Date Published: 2 December 2005
PDF: 9 pages
Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 604519 (2 December 2005); doi: 10.1117/12.651108
Show Author Affiliations
Weijin Jiang, Zhuzhou Institute of Technology (China)
Yusheng Xu, Beijing Univ. of Technology (China)
Yuhui Xu, Zhuzhou Institute of Technology (China)


Published in SPIE Proceedings Vol. 6045:
MIPPR 2005: Geospatial Information, Data Mining, and Applications
Jianya Gong; Qing Zhu; Yaolin Liu; Shuliang Wang, Editor(s)

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