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An Efficient MDS Algorithm for the Analysis of Massive Document Collections

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

In order to solve multidimensional scaling (MDS) efficiently, we proposed an algorithm, which apply stochastic gradient algorithm to minimizing well-known MDS criteria [1]. In this paper, the efficient MDS algorithm is applied to the text mining and compared with the SOM [2]. The results verified the validity of our algorithm in the analysis of a massive document collection. Our algorithm could find out some interesting structures from about 100000 articles in Usenet (NetNews).

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References

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Author information

Authors and Affiliations

  1. Kazunori Yamaguchi Laboratory, Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo, 153-8902, Japan

    Yoshitatsu Matsuda & Kazunori Yamaguchi

Authors
  1. Yoshitatsu Matsuda
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  2. Kazunori Yamaguchi
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Matsuda, Y., Yamaguchi, K. (2005). An Efficient MDS Algorithm for the Analysis of Massive Document Collections. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_140

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  • DOI: https://doi.org/10.1007/11552451_140

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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