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Data visualization is invaluable for explaining the significance of data to people who are visually oriented. The central task of automatic data visualization is, given a dataset, to visualize its compelling stories by transforming the data (e.g., selecting attributes, grouping and binning values) and deciding the right type of visualization (e.g., bar or line charts). We present DEEPEYE, a novel system for automatic data visualization that tackles three problems: (1) Visualization recognition: given a visualization, is it "good or "bad"? (2) Visualization ranking: given two visualizations, which one is "better"? And (3) Visualization selection: given a dataset, how to find top-k visualizations? DEEPEYE addresses (1) by training a binary classifier to decide whether a particular visualization is good or bad. It solves (2) from two perspectives: (i) Machine learning: it uses a supervised learning-to-rank model to rank visualizations; and (ii) Expert rules: it relies on experts' knowledge to specify partial orders as rules. Moreover, a "boring" dataset may become interesting after data transformations (e.g., binning and grouping), which forms a large search space. We also discuss optimizations to efficiently compute top-k visualizations, for approaching (3). Extensive experiments verify the effectiveness of DEEPEYE".
Luo et al. (Sun,) studied this question.