## Attribute Distance¶

Computes distances between examples in the data set

### Signals¶

- Inputs:
- Examples
A list of examples

- Outputs:
- Distance Matrix
A matrix of attribute distances

### Description¶

Widget Attribute Distances computes the distances between the attributes in the data sets. Don’t confuse it with a similar widget for computing the distances between examples.

Since the widget cannot compute distances between discrete and continuous
attributes, all attributes are first either discretized, by splitting the
attribute into four quartiles, or “continuized” by treating any discrete
attributes as ordinal with values equivalent to 0, 1, 2, 3... For other,
possibly better methods of discretization/continuization, see widgets
*Discretize* and *Continuize*.

The two kinds of attributes then have different measures of distance.

For discrete attributes, the distance can be computed as
`Pearson's chi-square`, where the more the two attributes are dependent,
the closer they are. The measure actually returns the p-value of the common
chi-square test of independence. The other two measures are as defined by
Aleks Jakulin in his work on attribute interactions: `2-way interaction` is
defined as I(A;B)/H(A,B) and `3-way interaction` is I(A;B;C),
respectively.

### Examples¶

This widget is an intermediate widget: it shows no user readable results and
its output needs to be fed to a widget that can do something useful with the
computed distances, for instance the *Distance Map*,
*Hierarchical Clustering* to cluster the attributes, or *MDS* to
visualize the distances between them.