@article{archambault2015,
title = {4.2 Machine Learning Meets Visualization: A Roadmap for Scalable Data Analytics},
author = {Archambault, Daniel and Bunte, Kerstin and Carreira-Perpi\~n\'an, Miguel \'A and Ebert, David and Ertl, Thomas and Zupan, Blaz},
journal = {Dagstuhl Reports: {Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)}},
pages = {7},
volume = {5},
issue = {3},
year = {2015},
editor = {Daniel A. Keim and Tamara Munzner and Fabrice Rossi and Michel Verleysen},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
url = {http://drops.dagstuhl.de/opus/volltexte/2015/5266},
urn = {urn:nbn:de:0030-drops-52665},
doi = {http://dx.doi.org/10.4230/DagRep.5.3.1},
annote = {Keywords: Information visualization, Machine learning, Visual data mining, Exploratory data analysis},
abstract = {The big data problem requires the development of novel analytic tools for knowledge discovery and data interpretation. The fields of visualization and machine learning have been addressing this problem from different perspectives and advances in both communities need to be leveraged in order to make progress. Machine learning has proposed algorithms that can address and represent large volumes of data enabling visualizations to scale. Conversely, visualization provides can leverage the human perceptual system to interpret and uncover hidden patterns in these data sets},
}