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Document Clustering Algorithm Based on Tree-Structured Growing Self-Organizing Feature Map

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

Document clustering is widely studied in text mining. In this paper, document clustering algorithm based on Tree-Structured Growing Self-organizing Feature Map (TGSOM) is presented as an extended version of the clustering algorithm of Self-organizing Map (SOM) in neural network, which has a dynamic tree-structure generated during the training process. TGSOM ’s growth speed can be controlled through the function of the Spread Factor (SF), and the precision of clustering results is different because of the difference value of SF. The user can get the hierarchical clustering results through changing the size of SF in different steps during clustering.

Supported by the technology development Foundation of Tianjin (No: 043600411)

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References

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Author information

Authors and Affiliations

  1. College of Marine Science and Engineering, Tianjin University of Science and Technology, 300450, Tianjin, China

    Xiaoshen Zheng & Wenling Liu

  2. Department of Computer Science and Technology, Tianjin University, 300072, Tianjin, China

    Pilian He & Weidi Dai

Authors
  1. Xiaoshen Zheng
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  2. Wenling Liu
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  3. Pilian He
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  4. Weidi Dai
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zheng, X., Liu, W., He, P., Dai, W. (2004). Document Clustering Algorithm Based on Tree-Structured Growing Self-Organizing Feature Map. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_138

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_138

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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