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Visualising the structure of document search results: A comparison of graph theoretic approaches

机译：可视化文档搜索结果的结构：图形理论方法的比较

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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.
机译：先前的工作表明，距离相似性可视化或“空间化”可以提供潜在有用的上下文，在其中可以浏览查询搜索的结果，从而使用户可以采用简单的本地搜寻或“集群增长”策略来浏览检索到的内容。文件集。但是，由于特征空间模型固有的高维和非线性特性，将它们正确地映射到视觉空间可能会出现问题。传统的降维线性方法往往无法完成此类任务，牺牲局部结构以保留全局最优映射。在本文中，将最近提出的称为等距特征映射（Isomap）的算法的聚类性能与通过非等距多维缩放（MDS）的聚类性能进行了比较，该算法通过将相异性转换为测地距离来处理非线性。还比较了用于测地距离估计的各种图修剪方法。结果表明，与MDS相比，Isomap在保留局部结构细节方面显着更好，这表明Isomap更适合于群集增长和其他语义导航任务。此外，已显示出应用最小代价图修剪准则可以提供传统K邻域方法的无参数替代方案，从而导致空间聚类等于或优于使用最佳K准则所实现的聚类。

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