TextLuas：动态文本数据中文档和术语簇的跟踪与可视化
TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data
摘要：
对于随着时间的推移收集的大量文本数据，一个关键的知识发现任务是识别和跟踪集群。这些聚类可能对应于语料库中的新兴主题、流行话题或突发新闻故事。因此，最近已经有越来越多的兴趣在动态数据的聚类问题。然而，存在很少的支持，这些分析技术的输出的交互式探索，特别是在研究人员希望同时探索随着时间的推移在集群结构的变化和变化的文本内容与集群的情况下。在本文中，我们提出了一个模型来跟踪动态集群的特点是每个集群的进化事件。受此模型的启发，TextLuas系统提供了一个实现，用于跟踪这些动态集群，并使用地铁地图隐喻来可视化它们的演变。为了提供集群内容的概述，我们适应标签云表示的动态聚类方案。我们展示了两个不同的文本语料库上的TextLuas系统，在那里他们被证明阐明关键主题的演变。我们还描述了如何将TextLuas应用于书目网络研究中的一个问题。
英文摘要：
For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.
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