InterAxis: Steering Scatterplot Axes via Observation-Level Interaction

Hannah Kim, Jaegul Choo, Haesun Park, Alex Endert
2016 IEEE Transactions on Visualization and Computer Graphics  
Fig. 1 . An overview of the proposed visual analytics system, InterAxis, showing a car dataset, which includes 387 data items with 18 attributes. The proposed system contains three panels: (A) the scatterplot view to provide a two-dimensional overview of data, (B-D) the axis interaction panel to support the proposed interaction capabilities, and (E) the data detail view to show the original high-dimensional information of the data items of interest. The axis interaction panel (B-D) consists of
more » ... B) two drop zones (the high-end and the low-end of each axis), which a user drags data points into in order to steer the axis, (C) an interactive bar chart, and a sub-panel containing buttons to save the current axis for future use (D, middle) or to clear the data points currently assigned to the axis (D, right) and a combo box to change the axis back to one among the original features or the previously created axes via our interaction (D, left). Abstract-Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes, from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios. Index Terms-Scatterplots, user interaction, model steering For information on obtaining reprints of this article, please send
doi:10.1109/tvcg.2015.2467615 pmid:26357399 fatcat:yvjc2pbacnhhrelfdcm327tyza