Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.

时空数据的搜索结果通常显示在地图上，但是当匹配搜索结果的数量很大时，即使使用诸如筛选搜索之类的方法来缩小内容焦点的范围，单独检查所有结果也可能很耗时。 。这表明需要通过聚类方法汇总结果。但是，标准的无监督聚类算法$ķ$-表示（i）忽略可能有助于提取高度相关的类的相关性得分，以及（ii）不一定出于视觉呈现目的而优化搜索结果。在本文中，我们通过在一个新颖的优化框架中为搜索驱动的用户界面框架化聚类问题来解决这两个缺陷，该框架（i）旨在根据基于聚类的标准信息检索指标扩展来最大化聚合内容的相关性，以及（ii ）通过约束来定义聚类，这些约束自然反映了界面驱动的空间，时间和关键字连贯性需求，而无需复杂的临时距离度量规范$ķ$-手段。在比较了我们提出的方法RadiCAL的算法变体后，在离线实验中，我们进行了一项针对24个受试者的用户研究，以评估RadiCAL与在视觉搜索任务上相比，是否可以改善人类在视觉搜索任务上的表现$ķ$-表示聚类和过滤后的搜索基准。我们的结果表明：（a）我们的RadiCAL二进制分区搜索（BPS）变体快速，接近最优，并且提取的相关性较高的簇比$ķ$-均值，以及（b）通过RadiCAL优化的集群可以更快，更准确地完成搜索任务，同时所需的工作量最少，从而在其他选择中带来了高效率，效率和用户满意度。