@inproceedings{Kassel2017,
	abstract = {Exploratory data analysis is an essential step in discovering patterns and relationships in data. However, the exploration may start without a clear conception about what attributes to pick or what visualizations to choose in order to develop an understanding of the data. In this work we aim to support the exploration process by automatically choosing attributes according to an information-theoretic measure and by providing a simple means of navigation through the space of visualizations. The system suggests data attributes to be visualized and the visualization's type and appearance. The user intuitively modifies these suggestions by performing swiping gestures on a tablet device. Attribute suggestions are based on the mutual information between multiple random variables (MMI). The results of a preliminary user study (N = 12 participants) show the applicability of MMI for guided exploratory data analysis and confirm the system's general usability (SUS score: 74).},
	title = {Immersive Navigation in Visualization Spaces through Swipe Gestures and Optimal Attribute Selection},
	author = {Kassel, Jan-Frederik and Rohs, Michael},
	booktitle = {Proceedings of the 2nd Workshop on Immersive Analytics: Exploring Future Interaction and Visualization Technologies for Data Analytics},
	location = {Phoenix, AZ, USA},
	year = {2017},
	papertype = {workshoppaper}
}