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A Model of a GEP-Based Text Clustering on Counter Propagation Networks

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Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 237))

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Abstract

In this paper, we present a model of a GEP-based test clustering research on counter propagation networks. The idea of the model is that it optimize the link weight vector by using the advantage of the GEP(Gene Expression Programming, GEP) in combinatorial optimization. We investigate how the value of weight in the network affect the performance of the text clustering by comparing it to a based on genetic algorithm and SOM network and the method of the traditional CPN(Counter Propagation Networks, CPN) .Furthermore, we improve and optimize the weight in the CPN network by the method of GEP, thus raise the quality of the text clustering in the network. Finally, in this paper, we demonstrated the validity and superiority of the presented model.

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Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, 530023, China

    Jin’guang Luo, Chang’an Yuan & Jinkun Luo

Authors
  1. Jin’guang Luo
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  2. Chang’an Yuan
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  3. Jinkun Luo
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Editor information

Editors and Affiliations

  1. School of Business Information technology, RMIT University, GPO Box 2476V, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. Department of Computer Science and Technology, Tongji University, Shanghai, China

    Duoqian Miao

  3. Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan, Hong Kong, SAR, China

    Fu Lee Wang

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

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© 2011 Springer-Verlag Berlin Heidelberg

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Luo, J., Yuan, C., Luo, J. (2011). A Model of a GEP-Based Text Clustering on Counter Propagation Networks. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_29

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  • DOI: https://doi.org/10.1007/978-3-642-24282-3_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24281-6

  • Online ISBN: 978-3-642-24282-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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