  Parallel coordinates plot is one of the most popular and widely used
visualization techniques for multi-dimensional data sets. Its main challenges
for large-scale data sets are visual clutter and overplotting which hamper the
recognition of patterns and trends in the data. In this paper, we propose a
confluent drawing approach of parallel coordinates to support the web-based
interactive visual analytics of large multi-dimensional data. The proposed
method maps multi-dimensional data to node-link diagrams through the data
binning-based clustering for each dimension. It uses density-based confluent
drawing to visualize clusters and edges to reduce visual clutter and
overplotting. Its rendering time is independent of the number of data items. It
supports interactive visualization of large data sets without hardware
acceleration in a normal web browser. Moreover, we design interactions to
control the data binning process with this approach to support interactive
visual analytics of large multi-dimensional data sets. Based on the proposed
approach, we implement a web-based visual analytics application. The efficiency
of the proposed method is examined through experiments on several data sets.
The effectiveness of the proposed method is evaluated through a user study, in
which two typical tasks of parallel coordinates plot are performed by
participants to compare the proposed method with another parallel coordinates
bundling technique. Results show that the proposed method significantly
enhances the web-based interactive visual analytics of large multi-dimensional
data.
