- Summary: Showing dependencies between two three dimensions
- Number of Dimensions: 2 plus 1 encoded by color
- Data Types: Numerical, Nominal, Dates
This is the next post of a series describing all RapidMiner plotters in detail. A list of the plotters discussed so far can be found at the end of this article including the links to them. Since many options and controls of these plotters are also relevant for the one discussed here - as well as for many other plotters - I recommend to check out the first parts of this series before reading this one.
Before we start our discussion about the Block plotter, we will again first have a look:
The block plotter is basically a scatter plot with two dimensions on the x-axis and the y-axis and one dimension used for the definition of the data points. The main difference to a scatter plot actually is the point format which is not a dot but a block. This is quite useful if the data set contains points on a two-dimensional grid.
Like the scatter plot, the block plot is a simple two-dimensional plot with two axes: x and y. The x-axis is plotted horizontally and the y-axis vertically. If you plot a data set, each point will be located at the position which corresponds to the values with respect to those two axes but instead of a point a block is printed.
As always, you can find two boxes where you can select the attributes (variables, dimensions) of your data set or model which should be used for the x-Axis and for the y-Axis. Those two options both have to be set, the plotter will not show anything otherwise. By the way, you can use numerical attributes as well as nominal attributes for the axes. Even date attributes are supported.
As you can see, you can also identify if the selected attribute should be transformed on a log scale. Just check the box below the corresponding axis.
The next option is called Color Column. If you select an attribute of your data or model here, the values of this attribute will be used for determining the color of each of the blocks.
The block plot also provides the Jitter option although it is certainly not used as often as for the scatter plot. However, this option is quite useful if several data points are located at the same point in the two-dimensional space. Just move around the jitter slider and look what's happening: the blocks are moving a bit to a random direction showing if and which points are lying below.
The last two options are pretty simple: Rotate Labels causes that the labels of the x-Axis are rotated by 90 degrees. Especially if you use a nominal attribute for the x-axis, the values can then be easily read. Export Image opens a dialog which allows you to export the current plotter with all its settings into one of the dozens supported image formats.
Of course the block plot also supports zooming and panning as described here .
Below you can find another useful example for the block plot, namely the visualization of a correlation matrix (here done for the data set "Sonar"). You can easily see on the diagonal that attributes close to each other are more correlated and that there also regions of attribute combinations with a high (negative) correlation:
Other parts of the plotter series: