In the final video in our Data Mining Fundamentals series, we conclude our discussion of different visualization techniques for data exploration with scatter plots and contour plots. We will define each plot, and share examples of when you can use each for your data mining. This completes our data mining series and you should now be ready to follow along at our data science bootcamp.
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
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
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
Another plot that we use a lot are contour plots,
Essentially, you can think of geographical maps here.
We use contour plots for topographical maps
So in this case, we partition the plane
into regions of similar values and color in those values,
separating them with little contour lines
So that was a very, very fast blast
through a number of different kinds of graphs.
And that concludes our webinar on the fundamentals
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.
Histograms & Box Plots
Data Mining Fundamentals
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