Advertisement

Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface

  • Julia Handl
  • Bernd Meyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Sorting and clustering methods inspired by the behavior of real ants are among the earliest methods in ant-based meta-heuristics. We revisit these methods in the context of a concrete application and introduce some modifications that yield significant improvements in terms of both quality and efficiency. Firstly, we re-examine their capability to simultaneously perform a combination of clustering and multi-dimensional scaling. In contrast to the assumptions made in earlier literature, our results suggest that these algorithms perform scaling only to a very limited degree. We show how to improve on this by some modifications of the algorithm and a hybridization with a simple pre-processing phase. Secondly, we discuss how the time-complexity of these algorithms can be improved. The improved algorithms are used as the core mechanism in a visual document retrieval system for world-wide web searches.

Keywords

Multidimensional Scaling Original Algorithm Document Retrieval Document Space Visualization Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. CC94.
    T.F. Cox and M.A.A. Cox. Multidimensional Scaling. Chapman & Hall, 1994.Google Scholar
  2. CD00.
    Hao Chen and Susan Dumais. Bringing order to the web. In ACM CHI, The Hague, April 2000.Google Scholar
  3. CDG99.
    D. Corne, M. Dorigo, and F. Glover, editors. New Ideas in Optimization, chapter 2: The Ant Colony Optimization Meta-Heuristic, pages 379–387. McGraw-Hill International (UK) Limited, 1999.Google Scholar
  4. Cha93.
    M. Chalmers. Using a landscape metaphor to represent a corpus of documents. In A. Frank and I. Campari, editors, Spatial Information Theory: A Theoretical Basis for GIS, pages 377–390. Springer-Verlag, September 1993.Google Scholar
  5. DDL+90.
    S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391–407, 1990.CrossRefGoogle Scholar
  6. Den90.
    J. L. Deneuborg. The dynamics of collective sorting. robot-like ants and ant-like robots. In 1st International Conference on Simulation of Adaptive Behaviour: From animals to animats 1, pages 356–363. MIT Press, Mai 1990.Google Scholar
  7. Fab00.
    S. I. Fabrikant. Spatial Metaphors for Browsing Large Data Archives. PhD thesis, Department of Geography, University of Colorado, 2000.Google Scholar
  8. Han.
    J. Handl. Visualising internet-queries using ant-based heuristics. Honours Thesis. Dept. of Computer Science, Monash University, Australia. 2001.Google Scholar
  9. KLS97.
    P. Kuntz, P. Layzell, and D. Snyers. A colony of ant-like agents for partitioning in VLSI technology. In 4th European Conference on Artificial Life. MIT Press, July 1997.Google Scholar
  10. KS94.
    P Kuntz and D. Snyers. Emergent colonization and graph partitioning. In 3rd International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3. MIT Press, April 1994.Google Scholar
  11. KS99.
    P. Kuntz and D. Snyers. New results on an ant-based heuristic for highlighting the organization of large graphs. In 99 Congress on Evolutionary Computation, pages 1451–1458. IEEE Press, July 1999.Google Scholar
  12. KSL98.
    P. Kuntz, D. Snyers, and P. Layzell. A stochastic heuristic for visualising graph clusters in a bi-dimensional space prior to partitioning. Journal of Heuristics, 1998.Google Scholar
  13. LA00.
    A. Leuski and J. Allan. Lighthouse: Showing the way to relevant information. In IEEE Information Vizualization, Salt Lake City, October 2000.Google Scholar
  14. Lag00.
    K. Lagus. Text Mining with the WEBSOM. PhD thesis, Department of Computer Science and Engineering, Helsinki University of Technology, 2000.Google Scholar
  15. LF94.
    E. Lumer and B. Faieta. Diversity and adaption in populations of clustering ants. In 3rd International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3. MIT Press, July 1994.Google Scholar
  16. MSV99.
    N. Monmarche, M. Slimane, and G. Venturini. On improving clustering in numerical databases with artificial ants. In Advances in Artificial Life (ECAL’99), LNAI1674. Springer-Verlag, 1999.Google Scholar
  17. NL01.
    D. J. Navarro and M. D. Lee. Spatial visualisation of document similarity. In Defence Human Factors Special Interest Group Meeting, August 2001.Google Scholar
  18. Sal88.
    G. Salton. Automatic Text Processing. Addison-Wesley, New York, 1988.Google Scholar
  19. ZE99.
    O. Zamir and O. Etzioni. Grouper: A dynamic clustering interface to web search results. In 8th World Wide Web Conference, Toronto, May 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Julia Handl
    • 1
  • Bernd Meyer
    • 2
  1. 1.FB InformatikUniversität Erlangen-NürnbergNürnberg
  2. 2.School of Computer ScienceMonash UniversityAustralia

Personalised recommendations