  Multi-dimensional data exploration is a classic research topic in
visualization. Most existing approaches are designed for identifying record
patterns in dimensional space or subspace. In this paper, we propose a visual
analytics approach to exploring subset patterns. The core of the approach is a
subset embedding network (SEN) that represents a group of subsets as
uniformly-formatted embeddings. We implement the SEN as multiple subnets with
separate loss functions. The design enables to handle arbitrary subsets and
capture the similarity of subsets on single features, thus achieving accurate
pattern exploration, which in most cases is searching for subsets having
similar values on few features. Moreover, each subnet is a fully-connected
neural network with one hidden layer. The simple structure brings high training
efficiency. We integrate the SEN into a visualization system that achieves a
3-step workflow. Specifically, analysts (1) partition the given dataset into
subsets, (2) select portions in a projected latent space created using the SEN,
and (3) determine the existence of patterns within selected subsets. Generally,
the system combines visualizations, interactions, automatic methods, and
quantitative measures to balance the exploration flexibility and operation
efficiency, and improve the interpretability and faithfulness of the identified
patterns. Case studies and quantitative experiments on multiple open datasets
demonstrate the general applicability and effectiveness of our approach.
