We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human‐subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components, and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state‐of‐the‐art merging techniques (Demp). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state‐of‐the‐art clustering measures, including the well‐known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.

中文翻译：

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ClustMe：基于聚类模式对单色散点图进行排名的视觉质量度量

我们提出了 ClustMe，这是一种新的视觉质量度量，可根据集群模式对单色散点图进行排名。ClustMe 基于从人类受试者研究中收集的数据，其中 34 名参与者判断了 1000 个散点图中合成生成的聚类模式。我们通过仔细改变具有两个分量的简单高斯混合模型的自由参数来生成这些模式，并要求参与者计算他们可以看到的集群数量（1 个或超过 1 个）。根据结果​​，我们通过在 7 种不同的最先进的合并技术 (Demp) 中选择最能预测这些人类判断的模型来形成 ClustMe。为了定量评估 ClustMe，我们进行了第二项研究，其中 31 名人类受试者根据聚类模式复杂性对 435 对真实和合成数据的散点图进行了排名。我们使用这些数据将 ClustMe 的性能与其他 4 种最先进的聚类措施进行比较，包括著名的 Clumpiness scagnostics。我们发现在所有指标中，ClustMe 与人类排名的一致性最强。