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

• Published in: Computer graphics forum Vol. 36; no. 8; pp. 458 - 486
• Main Authors: Endert, A., Ribarsky, W., Turkay, C., Wong, B.L. William, Nabney, I., Blanco, I. Díaz, Rossi, F.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.12.2017; Wiley
• Subjects: Analytics; Artificial intelligence; Categories and Subject Descriptors: Human‐Centred Computing; Computer Science; data mining; Human-Computer Interaction; information visualization; Interactive systems; Machine Learning; Mathematical analysis; Scientific visualization; State of the art; Statistics; visual analytics; Visualization; Visualization; Information visualization; Visual Analytics; data mining; Human-Centred Computing
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.13092

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. 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.