Exploring Multi-dimensional Data via Subset Embedding

• URL: http://arxiv.org/abs/2104.11867v1
• Date: Sat, 24 Apr 2021 03:08:08 GMT
• Title: Exploring Multi-dimensional Data via Subset Embedding
• Authors: Peng Xie, Wenyuan Tao, Jie Li, Wentao Huang, Siming Chen
• Abstract summary: 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. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features.
• Score: 13.092303047029311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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