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Toward Responsive Visualization Services for Scatter/Gather Browsing

机译：对响应性可视化服务，用于分散/聚集浏览

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As a type of relevance feedback, Scatter/Gather demonstrates an interactive approach to relevance mapping and reinforcement. The Scatter/Gather model, proposed by Cutting, Karger, Pedersen, and Tukey (1992), is well known for its effectiveness in situations where it is difficult to precisely specify a query. However, online clustering on a large data corpus is computationally complex and extremely time consuming. This has prohibited the method's real world application for responsive services. In this paper, we proposed and evaluated a new clustering algorithm called LAIR2, which has linear worst-case time complexity and constant running time average for Scatter/Gather browsing. Our experiment showed when running on a single processor, the LAIR2 online clustering algorithm is several hundred times faster than a classic parallel algorithm running on multiple processors. The efficiency of the LAIR2 algorithm promises real-time Scatter/Gather browsing services. We have implemented an online visualization prototype, namely, LAIR2 Scatter/Gather browser, to demonstrate its utility and usability.
机译：作为一种相关反馈的类型，散射/聚集展示了相关映射和加强的交互式方法。通过切割，karger，pedersen和tukey（1992）提出的散射/聚集模型，以其在难以准确指定查询的情况下的有效性众所周知。但是，在大数据上的在线聚类是计算的复杂性和非常耗时。这禁止该方法对响应服务的真实世界申请。在本文中，我们提出并评估了一种名为Lair2的新集群算法，其具有线性最坏情况的时间复杂度和持续运行的散射/聚集浏览的时间平均值。我们的实验显示在单个处理器上运行时，Lair2在线聚类算法比在多个处理器上运行的经典并行算法快几百倍。 Lair2算法的效率承诺实时分散/收集浏览服务。我们已经实施了在线可视化原型，即Lair2 Scatter / Gather浏览器，以展示其实用性和可用性。

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