The State of the Art in Integrating Machine Learning into Visual Analytics

TL;DR

This paper reviews the integration of machine learning techniques into visual analytics systems, highlighting recent advances, challenges, and future research opportunities to improve data understanding and decision-making.

Contribution

It provides a comprehensive synthesis of current research progress and identifies key opportunities and challenges in combining machine learning with visual analytics.

Findings

Progress has been made in integrating ML with visual analytics.

Challenges include effective synergy and interpretability.

Future directions involve addressing these challenges for impactful research.

Abstract

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.