Parallel Coordinates is an often used visualization method for multidimensional data sets.
Its main challenges for large data sets are visual clutter and over-plotting which hamper the recognition of patterns in the data.
We present an edge-bundling method using density-based clustering for each dimension.
This reduces clutter and provides a faster overview of clusters and trends.
Moreover, it allows rendering the clustered lines using polygons, decreasing rendering time remarkably.
In addition, we design interactions to support multidimensional clustering with this method.
Following is the comparison of the different PCP layouts. Shown are three
dimensions from the Cars data set.
Check the videos here :