Splatting the Lines in Parallel Coordinates

Abstract

In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by splatting the lines one by one into the parallel coordinates plots, and for each splatted line we enhance its neighboring lines and suppress irrelevant ones. To reduce visual clutter caused by line crossings and overlappings in the clustered results, we provide a segment splatter which represents each polyline by one segment and splats these segments with different speeds, colors, and lengths from the leftmost axis to the rightmost axis. Users can interactively control both the polyline splatting and the segment splatting processes to emphasize the features they are interested in. The experimental results demonstrate that our framework can effectively reveal some hidden patterns in parallel coordinates.

Adaptive Privacy-Preserving Visualization Using Parallel Coordinates

“…Scaling the same range of alpha values over larger clusters produces more fuzziness, while smaller clusters appear sharper (Figure 6(b)). The overall effect of many overlapping clusters is similar to splatting [28].…”

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

“…This can be accomplished using density-based methods [8,12,13] or curves instead of lines [14][15][16]. However, such methods do not scale as they are, since they require to draw every item.…”

“…It is difficult to obtain the same information on Figure 7a as many lines overlap. This is a well-known limit of classical parallel coordinates and modern state-of-the-art techniques do present solutions to this issue (using density [12], curves [14] for example). If we consider the two attributes of retail price and dealer cost (surrounded in red on Figure 7a,b), we observe with both techniques, line-based and abstracted, that meta-links between the two axes are parallel.…”

A Five-Level Design Framework for Bicluster Visualizations

“…In this way, possible biclusters can be explored by starting analysis from the aggregated curved lines. The third way is to aggregate entities and polylines respectively and use colored ribbons, similar to the Bubble Sets technique [19], to wrap the aggregated entities and polylines from the first vertical axis to the final one [3,31,55,58,65,92], which can be used to indicate a bicluster that is determined by the smallest set of the shared entities across all axes in this region. Finally, tile-based parallel coordinates [2] provides an efficient way to avoid visual clutter, since it divides the plotting space into rectangular tiles and colors these tiles based on the sum of polylines that intersect with the tile.…”