|
|
|
Let me show you a
demo of the rank-by-feature framework with a small data set.
|
|
|
|
<<demo>>
|
|
(Table View)
This is the raw data. Each row is a
breakfast cereal and each column is a nutrition component.
|
|
(Scatterplot
Ordering) This is our
rank-by-feature framework interface for 2D scatterplots.
|
|
( move from top to
bottom at the list view ) There are 36 possible 2D scatterplots for
this data sets.
|
|
Each cell of this
view represents a 2D scatterplot. For example, this cell is for protein and
fat.
|
|
These three views
are coordinated. So you can easily
change and see projections.
|
|
But even small data
sets like this data is not easy to identify interesting projections.
|
|
So what is our
solution? Here is the key. Users can select a feature from this combo
box.
|
|
(combo box)
Let’s select correlation coefficient here.
|
|
(prism) This
view, we call it feature prism, shows the overview of the score distribution,
in this case correlation coefficient.
|
|
If a cell is bright,
the corresponding projection gets a high score.
|
|
We can easily find
that this cell the most bright. We can know that the amount of potassium and
dietary fiber are highly correlated.
|
|
We can also easily
identify scatterplots that show negative correlations. Carbohydrate and
dietary fiber.
|
|
(browser) In
this view called scatterplot browser, you can easily change the variable for
each axis by just dragging this slider.
|
|
|
|
Let me get back to
the slides.
|
|
|
|
|
