You can export the view contents to either the PNG or SVG files from the File | Export as menu. (The latter is only available when the KNIME SVG Support is installed.)
It is worth noting the other usual view controls. The File menu contains the Always on top and Close options, besides the previously discussed Export as menu. The first option allows you to compare the multiple views easily by having them side-by-side and still working with other windows.
The rest of the menus are related to HiLiting, which will be discussed soon.
The configuration of nodes usually includes an option of how many different values or how many rows should be used when you create the view. Because the views usually load all the data (or the specified amount) in the memory to have a resizable content, too many rows would require too much memory, while too many different values would make it hard to understand either the legends or the whole view in certain cases.
The mouse mode controls allow you to select certain points or set of points (for example, in the case of hierarchical clustering and the histogram nodes), to zoom in or to move around in a zoomed view. With the Background Color option, you can change the background of the plot. The Use anti-aliasing option can be used to apply subpixel rendering for fonts and lines.
The HiLite menu consists of the HiLite Selected, UnHiLite Selected, and Clear HiLite items. With these items, you can create fine-grained HiLite rows. Once you select a few data points/rows, you can add or remove the HiLite signal using the first two options, and the third clears all the HiLite signals from this part of the workflow.
Lots of the nonview nodes also have HiLite-related options, which can be very handy when the row's IDs change and want to propagate HiLiting to the parts with different row IDs of the workflow; however, beware, as this usually requires additional memory.
The Show/Hide menu (or the HiLite/Filter menu) also helps the HiLite operations. The Show hilited only option hides all the non-HiLited rows/points. The default option is usually Show all, but the Fade unhilited option is a compromise between the two (shows both the kinds of data, but the non-HiLited are faded or grey).
You might wonder how this HiLite feature is useful.
With the Box Plot and the Conditional Box Plot nodes, you can select the rows that have extreme values in certain columns or extreme values within a class without creating complex filtering. (The extremity is defined as below 
or as above 
It is also useful to see the same selection of data from different perspectives. For example, you have the extremes selected based on some columns, but you are curious to know how they relate to other columns' values. The Parallel Coordinates or the Line Plot can give a visual overview of the values. The Scatter Plot (or the Scatter Matrix) node is also useful when different columns should be compared.
When you prefer the numeric/textual values of the selected rows, you should use the Interactive Table node. It allows you to check the HiLited and non-HiLited rows together or independently with the order of the column you want.
With the Hierarchical Clustering View node, you can select certain clusters (similar rows). This can also be useful to identify the outlier groups based on multiple columns (as the distances can be computed from more than one columns).
It is important to remember that the row IDs play an important role for most of the KNIME views. The row IDs are used as axis values; that is, tooltips. So, to create a nice, easy-to-understand figure/view, you have to provide as many useful row IDs as you can.
To use meaningful labels, you have to create a column with the proper (unique) values, and make that column a row ID with the help of the RowID node. This node also offers HiLite support (Enable Hiliting), so you do not have to make a compromise between neat figures and HiLiting.
The infinite values (Double.POSITIVE_INFINITY and Double.NEGATIVE_INFINITY) make the ranges meaningless, because these values are not measurable by normal real values.
The other special value is the Double.NaN (not a number) value, which you get, for example, when you divide zero by zero. It is not equal to any numeric value, not even to itself. It also makes comparison impossible, so it should be avoided as much as possible. The previous chapter has already introduced how to handle these cases.
The missing values are usually handled by not showing the rows containing them, but some views make it possible to use different strategies.