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

A. Endert (1) , W. Ribarsky (2) , C. Turkay (3) , W Wong (4) , I. Nabney (5) , I Díaz Blanco (6) , Fabrice Rossi (7)

1 Georgia Institute of Technology [Atlanta]
2 UNC - University of North Carolina [Charlotte]
3 City University London
4 Middlesex University
5 Aston University [Birmingham]
6 University of Oviedo
7 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

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.

Mots clés: Information visualization; data mining; Human-Centred Computing; Visualization; Visual Analytics

Domaines: Machine Learning [stat.ML]; Apprentissage [cs.LG]; Interface homme-machine [cs.HC]

A. Endert, W. Ribarsky, C. Turkay, W Wong, I. Nabney, et al.. The State of the Art in Integrating Machine Learning into Visual Analytics. Computer Graphics Forum, 2017, 36 (8), pp.458 - 486. ⟨10.1111/cgf.13092⟩. ⟨hal-01714743⟩