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Parallel coordinates plot is one of the most popular and widely usedvisualization techniques for multi-dimensional data sets. Its main challengesfor large-scale data sets are visual clutter and overplotting which hamper therecognition of patterns and trends in the data. In this paper, we propose aconfluent drawing approach of parallel coordinates to support the web-basedinteractive visual analytics of large multi-dimensional data. The proposedmethod maps multi-dimensional data to node-link diagrams through the databinning-based clustering for each dimension. It uses density-based confluentdrawing to visualize clusters and edges to reduce visual clutter andoverplotting. Its rendering time is independent of the number of data items. Itsupports interactive visualization of large data sets without hardwareacceleration in a normal web browser. Moreover, we design interactions tocontrol the data binning process with this approach to support interactivevisual analytics of large multi-dimensional data sets. Based on the proposedapproach, we implement a web-based visual analytics application. The efficiencyof the proposed method is examined through experiments on several data sets.The effectiveness of the proposed method is evaluated through a user study, inwhich two typical tasks of parallel coordinates plot are performed byparticipants to compare the proposed method with another parallel coordinatesbundling technique. Results show that the proposed method significantlyenhances the web-based interactive visual analytics of large multi-dimensionaldata.

Confluent-Drawing Parallel Coordinates: Web-Based Interactive Visual Analytics of Large Multi-Dimensional Data