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Title: Visualising the structure of document search results: A comparison of graph theoretic approaches
Authors: Cribbin, T
Keywords: Information retrieval;Document visualisation;Multidimensional scaling;Isometric feature mapping;Minimum spanning tree;Pathfinder associative network
Issue Date: 2010
Publisher: Sage Publications
Citation: Information Visualization, 9(2): 83 - 97, Jun 2010
Abstract: Previous work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion.
Description: This is the post-print of the article - Copyright @ 2010 Sage Publications
ISSN: 1473-8716
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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