Exploring Multi-dimensional Data via Subset Embedding

TL;DR

This paper introduces a novel visual analytics approach using a subset embedding network (SEN) for exploring patterns in multi-dimensional data, enabling efficient and interpretable subset pattern discovery.

Contribution

The paper presents a new subset embedding network design that handles arbitrary subsets and captures feature-based similarities, integrated into a visual system for effective data exploration.

Findings

Effective pattern exploration in multi-dimensional data

High training efficiency due to simple subnet structure

Demonstrated applicability on multiple datasets

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.