[1] Guo Yuqin, Yuan Fang, Liu Haibo, et al. Text categorization based on fuzzy classification rules tree [J]. Journal of Southeast University (English Edition), 2008, 24 (3): 339-342. [doi:10.3969/j.issn.1003-7985.2008.03.021]

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Text categorization based on fuzzy classification rules tree()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 3

Page:

339-342

Research Field:

Computer Science and Engineering

Publishing date:

2008-09-30

Info

Title:

Text categorization based on fuzzy classification rules tree

Author(s):

Guo Yuqin¹;  2;  Yuan Fang¹;  Liu Haibo¹

¹ College of Mathematics and Computer Science, Hebei University, Baoding 071002, China
² Tianjin Branch of the People’s Bank of China, Tianjin 300040, China

Keywords:

text categorization;  fuzzy classification association rule;  classification rules tree;  fuzzy classification rules tree

PACS:

TP393

DOI:

10.3969/j.issn.1003-7985.2008.03.021

Abstract:

To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts, which has low efficiency, a new approach based on the FCR-tree(fuzzy classification rules tree)for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules, the fuzzy classification rules contain not only words, but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore, the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval, more k-FCR-trees are built.When classifying a new text, it is not necessary to search the paths of the sub-trees led by those words not appearing in this text, thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.

References:

[1] Wang Yuanzhen, Qian Tieyun, Feng Xiaonian.Association rules based automatic Chinese text categorization [J].Mini-Micro Systems, 2005, 26(8):1380-1383.(in Chinese)
[2] Antonie M L, Zaiane O R.Text document categorization by term association [C]//Proc of the IEEE International Conference on Data Mining(ICDM’02).Maebashi City, Japan, 2002:19-26.
[3] Yuan Fang, Guo Yuqin, Yang Liu, et al.Chinese text categorization based on fuzzy association rules [C]//Proc of International Conference on Machine Learning and Cybernetics. Dalian, China, 2006:1030-1035.
[4] Li Wenmin, Han Jiawei, Pei Jian.CMAR:accurate and efficient classification based on multiple class-association rules [C]//Proc of the IEEE International Conference on Data Mining(ICDM’01). San Jose, CA, USA, 2001:369-376.
[5] Chen Xiaoyun, Chen Yi, Wang Lei, et al.Text categorization based on classification rules tree by frequent patterns[J].Journal of Software, 2006, 17(5):1017-1025.(in Chinese)
[6] Song Yuqing, Wang Lijun, Lü Ying, et al.Efficient association rule mining algorithm based on classification tree [J].Journal of Jiangsu University: Natural Science Edition, 2006, 27(1):51-54.(in Chinese)

Memo

Memo:

Biographies: Guo Yuqin(1981—), female, graduate;Yuan Fang(corresponding author), male, doctor, professor, yuanfang@hbu.cn.
Foundation items: The National Natural Science Foundation of China(No.60473045), the Technology Research Project of Hebei Province(No.05213573), the Research Plan of Education Office of Hebei Province(No.2004406).
Citation: Guo Yuqin, Yuan Fang, Liu Haibo.Text categorization based on fuzzy classification rules tree[J].Journal of Southeast University(English Edition), 2008, 24(3):339-342.

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