Another kind of plot that we
use a lot are scatter plots.
So, we allow our
attribute values
to determine the position.
We pick two attribute
values and we
plot the two values against each
other for every data object.
We can also use size,
shape, and color
of our markers to display
supplementary attributes.
This allows us to
construct three-
or four-dimensional graphs
on a two-dimensional plane
very easily.
And in particular,
we will see arrays
of scatter plots used
quite often as a way
to compactly summarize
our factor relationships.
So here's an example
of that same iris
data set and a scatter
plot of the attributes.
So we've got every attribute
plotted against the others.
So we've got sepal width
and sepal length, and then
sepal width and petal length,
and then sepal width and petal
width here.
And the color and
shape of our markers
tells us what the
species of the plant is.
So we can see, for
instance, that sepal length
and petal width, pedal
width in particular,
if we look at the petal
width row and column,
seems to be a very good
predictor for at least
the setosa species.
Another plot that we use
a lot are contour plots,
which we've seen before.
Essentially, you can think
of geographical maps here.
We use contour plots
for topographical maps
all the time.
So in this case, we
partition the plane
into regions of similar values
and color in those values,
separating them with
little contour lines
to show the differences.
All right.
So that was a very,
very fast blast
through a number of
different kinds of graphs.
And that concludes our
webinar on the fundamentals
of data mining.
Thank you for taking the time
to watch this presentation.
Please check out the next video
in our introductory series,
introduction to R.
Have a nice day.
