The R plot, Python plot, and Matlab plot are available from the corresponding scripting extensions (the KNIME R Scripting extension, KNIME Python Scripting extension, and KNIME Matlab Scripting extension) on the community nodes update site.

The usage of these nodes do not require experience in the corresponding programming languages. There are templates from which you can choose and the parameters can be adjusted using KNIME controls. Obviously, you can create your own templates or fine-tune existing ones if you are not satisfied.

You need to have access to (possibly local) servers to connect to the extensions. (The Python Plot node uses (C)Python with some extensions.)

These nodes also generate images as their outputs in the PNG format.

Please take a look at their figure template gallery (http://idisk-srv1.mpi-cbg.de/knime/scripting-templates_public/figure-template-gallery.html) to get an idea of what is possible and how they look.

The KNIME R Statistics Integration extension from the main KNIME update site offers similar options like the R Plot discussed previously, but it does require some R programming knowledge (the templates help the configuration).

When you want to use it locally, you will need the Table R-View node, but when you use an R server, you should use the R View (Remote) node. The result is also available in the PNG format.

The recently introduced R View and other interactive KNIME nodes offer other options for the visualization of data. For details, please check KNIME's site at http://tech.knime.org/whats-new-in-knime-28

The RapidMiner Viewer node is available on the community nodes and offers the Plot View and the Advanced Charts modes to visualize the data using RapidMiner's results view. It requires some pre-configuration, but after that, you will have a powerful tool for visual data exploration. (Unfortunately, it does not use many KNIME features; it neither supports HiLiting, color, shape, or size properties, nor provides the figure as an image.)

The views offer a wide range of visualization options and give highly customizable figures. It can even de-pivot in the view, so you do not have to create complex workflows to get an overview of the data. This view supports the following plots: Scatter, Scatter Multiple, Scatter Matrix, Scatter 3D, Scatter 3D Color, Bubble, Parallel, Deviation, Series, Series Multiple, Survey, SOM, Block, Density, Pie, Pie 3D, Ring, Bars, Bars Stacked, Pareto, Andrews Curves, Distribution, Histogram, Histogram Color, Quartile, Quartile Color, Quartile Color Matrix, Sticks, Sticks 3D, Box, Box 3D, and Surface 3D.

The Advanced Charts also support multiple visualizations. You can set the color, shape, and the size dimensions, although these are not auto-populated by the available properties. With the Advanced Charts, the details of the diagram can be configured in more depth than with the JFreeChart. It is worth reading the user manual of RapidMiner in this regard at http://docs.rapid-i.com/files/rapidminer/RapidMiner-5.2-Advanced-Charts-english-v1.0.pdf.

This node allows you to export the figure (without the controls) in various image formats. It is available from the icon in the upper-right corner.

The HiTS visualization might not fit the previous extensions as it is not available on the usual KNIME update sites. But it might bring your attention to look for alternative options when you need a functionality, because there are many KNIME nodes available besides the one we saw in the previous sections.

The HiTS extension's website is https://code.google.com/p/hits/. The update site is http://hits.googlecode.com/svn/trunk/ie.tcd.imm.hits.update/. On the website, look for the HiTS experimental features (and also check its dependencies: HiTS main feature and HiTS third party components feature) in the HiTS main category.

The Plate Heatmap node might not be so interesting, because it is quite specific to high content/throughput screening, but the Simple Heatmap and the Dendrogram with Heatmap nodes are generally useful. These support the HiLite feature and give an overview about the data with color codes.

The Dendrogram with Heatmap node uses the hierarchical clustering model to show the dendrogram. Together with the heatmap, it gives you a better idea about your clusters.