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Parallel coordinates are great for visualizing large multivariate data. These visualizations are most effective when they are interactive. That way, a user can dynamically filter, rearrange, and analyze the data to gain insights about these typically unwieldy data sets.
Since the best practices around parallel coordinates plots are still in active development, we've created Parasol to incorporate state of the art techniques that we have seen in the literature.
Specifically, we have developed Parasol with multi-objective decision making problems in mind. Multi-objective optimization methods have advanced significantly in recent years and are popular for discovering solutions to complex environmental management problems. Since multi-objective optimization algorithms tend to produce large, high dimensional data sets, parallel coordinates have grown popular in the field. However, there aren't many good ways to visualize these plots interactively. Trying to implement the latest and greatest features for parallel coordinate plots? Forget it.
Parasol offers developers to create web-based parallel coordinates visualizations that include these features and are easily shared with stakeholders, decision makers, and analysts involved in the decision making process.
Building off a great visualization libraries like D3, Parcoords, and SlickGrid, Parasol makes it easier to link interactive plots and tables together and incorporates clutter reduction techniques like clustering, curve bundling, marking, and highlighting.
To get started developing with Parasol, please reference the API and tutorials on the sidebar.