Cluster separation in scatterplots is a task that is typically tackled by
widely used clustering techniques, such as for instance k-means or DBSCAN.
However, as these algorithms are based on non-perceptual metrics, their output
often does not reflect human cluster perception. To bridge the gap between
human cluster perception and machine-computed clusters, we propose a learning
strategy which directly operates on scattered data. To learn perceptual cluster
separation on this data, we crowdsourced a large scale dataset, consisting of
7,320 point-wise cluster affiliations for bivariate data, which has been
labeled by 384 human crowd workers. Based on this data, we were able to train
ClusterNet, a point-based deep learning model, trained to reflect human
perception of cluster separability. In order to train ClusterNet on human
annotated data, we omit rendering scatterplots on a 2D canvas, but rather use a
PointNet++ architecture enabling inference on point clouds directly. In this
work, we provide details on how we collected our dataset, report statistics of
the resulting annotations, and investigate perceptual agreement of cluster
separation for real-world data. We further report the training and evaluation
protocol of ClusterNet and introduce a novel metric, that measures the accuracy
between a clustering technique and a group of human annotators. Finally, we
compare our approach against existing state-of-the-art clustering techniques.

本论文介绍了一种新的基于广泛使用的聚类技术 k-means 和 DBSCAN 局限性的聚类算法，该算法直接对散点数据进行操作，利用众包方法实现对人类视觉集群感知的学习策略，并使用基于点云的深度学习模型将其训练，以反映群集可分离性的人类感知。