|Table of Contents|

[1] Zhang Weifeng, , Xu Baowen, et al. Document classification approachby rough-set-based corner classification neural network [J]. Journal of Southeast University (English Edition), 2006, 22 (3): 439-444. [doi:10.3969/j.issn.1003-7985.2006.03.032]
Copy

Document classification approachby rough-set-based corner classification neural network()
Share:

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

Volumn:
22
Issue:
2006 3
Page:
439-444
Research Field:
Automation
Publishing date:
2006-09-30

Info

Title:
Document classification approachby rough-set-based corner classification neural network
Author(s):
Zhang Weifeng1 2 3 Xu Baowen2 3 Cui Zifeng2 3 Xu Junling 2 3
1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2College of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3Jiangsu Institute of Software Quality, Nanjing 210096, China
Keywords:
document classification neural network rough set meta search engine
PACS:
TP183
DOI:
10.3969/j.issn.1003-7985.2006.03.032
Abstract:
A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding.In the Rough-CC4, the documents are described by the equivalent classes of the approximate words.By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words.In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded.By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced.The Rough-CC4 can be used in automatic classification of documents.

References:

[1] Karypis G, Han E H, Kumar V.CHAMELEON:a hierarchical clustering algorithm using dynamic modeling(No.99-007)[R].Department of Computer Science and Engineering of University of Minnesota, 1999.
[2] Guha S, Rastogi R, Shim K.CURE:an efficient clustering algorithm for large databases [A].In:Proc of the ACM SIGMOD Int’l Conf on Management of Data[C].Seattle, 1998.73-84.
[3] Zhang T, Ramakrishnan R, Livny M.BIRCH:an efficient data clustering method for very large databases [A].In:Proc of the ACM SIGMOD Int’l Conf on Management of Data[C].Montreal, Canada, 1996.103-114.
[4] Kamber M. Data mining concepts and techniques [M].Translated by Fan M, Meng X F. Beijing:China Machine Press, 2001.(in Chinese).
[5] Ordonez C, Omiecinski E.FREM:fast and robust EM clustering for large data sets [A].In:Proc of the ACM CIKM Int’l Conf on Information and Knowledge Management[C].McLean, 2002.590-599.
[6] Hinneburg A, Keim D.An efficient approach to clustering in large multimedia databases with noise [A].In:Proc of the 4th Int’l Conf on Knowledge Discovery and Data Mining (KDD’98)[C].New York:AAAI Press, 1998.58-65.
[7] Ankerst M, Breunig M M, Kriegel H P, et al.OPTICS:ordering points to identify the clustering structure [A].In:Proc of ACM SIGMOD Int’l Conf on Management of Data[C].Philadelphia, 1999.49-60.
[8] Ester M, Kriegel H, Sander J, et al.A density-based algorithm for discovering clusters in large spatial databases with noise [A].In:Proc of the 2nd Int’l Conf on Knowledge Discovery and Data Mining (KDD’96)[C].Portland, 1996.226-231.
[9] Song Q B, Shen J Y.A web document clustering algorithm based on association rule [J].Journal of Software, 2002, 13(3):417-423.(in Chinese)
[10] Wang W, Yang J, Muntz R R.STING:a statistical information grid approach to spatial data mining [A].In:Proc of the 23rd Int’l Conf on Very Large Data Bases[C].Athens, 1997.186-195.
[11] Sheikholeslami G, Chatterjee S, Zhang A D.WaveCluster:a multi-resolution clustering approach for very large spatial databases [A].In:Proc of the 24th Int’l Conf on Very Large Data Bases[C].New York, 1998.428-439.
[12] Rakesh A, Johanners G, Dimitrios G, Prabhakar R.Automatic subspace clustering of high dimensional data for data mining applications [A].In:Proc of the ACM SIGMOD Int’l Conf on Management of Data [C].Minneapolis, 1994.94-105.
[13] Shu B, Kak S.A neural network-based intelligent meta search engine [J].Information Sciences, 1999, 120(1):1-11.
[14] Chen Enhong.An extended corner classification neural network based classification approach [J].Journal of Software, 2002, 13(5):871-878.
[15] Pawlak, Z.Rough sets [J].International Journal of Computer and Information Sciences, 1982, 11(5):341-356.

Memo

Memo:
Biographies: Zhang Weifeng(1975—), male, doctor, associate professor, wfzhang@yahoo.com;Xu Baowen(1961—), male, doctor, professor, bwxu@seu.edu.cn.
Last Update: 2006-09-20