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Adaptive topological tree structure for document organisation and visualisation.

Richard T Freeman1, Hujun Yin

  • 1Department of Electrical and Electronic Engineering, University of Manchester Institute of Science and Technology, PO Box 88, Manchester M60 1QD, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|November 24, 2004
PubMed
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A new Adaptive Topological Tree Structure (ATTS) method organizes unstructured documents into a browsable topic hierarchy. This approach improves document clustering, visualization, and information retrieval efficiency.

Area of Science:

  • Artificial Intelligence
  • Information Science
  • Computer Science

Background:

  • Self-Organising Maps (SOMs) are widely used for clustering, pattern recognition, and visualization.
  • Existing SOM-based methods have limitations for analyzing large sets of unstructured documents.
  • Knowledge management and information retrieval benefit from effective document organization.

Purpose of the Study:

  • To propose an alternative to 2D SOM-based methods for document analysis.
  • To introduce the Adaptive Topological Tree Structure (ATTS) for generating document topic taxonomies.
  • To enhance document organization, visualization, and retrieval capabilities.

Main Methods:

  • The ATTS method utilizes a hierarchy of adaptive self-organising chains.

Related Experiment Videos

  • Each chain is validated using an entropy-based Bayesian information criterion.
  • Nodes meeting expansion criteria generate child chains with specialized vocabularies.

    • Main Results:

    • The ATTS method creates a topological tree reflecting topic connections at multiple levels.
    • Experimental results on real-world datasets demonstrate ATTS efficiency.
    • The proposed validation criteria improve clustering and retrieval performance.

    Conclusions:

    • The ATTS approach offers an efficient method for document organization and visualization.
    • The ATTS method significantly enhances information retrieval.
    • This novel approach provides a browsable content hierarchy for unstructured documents.