Name

Enter a unique identifier for the attribute.

Type

Change the attribute type. 

Note that an incorrect data type may have been incorrectly assigned automatically during the data import operation, due to one or more values having been inserted with the incorrect format.

If you try to change the data type to the correct type, Rulex will tell you which row contains the format error.

For possible attribute types and their meanings see Datasets and Attributes. 

Role

Change the role of the attribute.

For possible attribute roles and their meanings see Datasets and Attributes. 

Ignore

If selected, the attribute is not considered in modeling operations.

Label

If selected, the attribute is considered as a unique identifier of the pattern, and rows are first grouped according to their labels and then processed. Consequently each label combination corresponds to a row in the dataset. 

This function is used by only some advanced clustering tasks, and should generally be left unchecked by default.

Use position to locate the attribute

If selected, the attribute is located by its position and not by its name.

When the property of an attribute is modified, such as its role or type, the name of the attribute is normally used as a reference. If the operation is copied and pasted into another Data Manager, the operation will be performed on the attribute with the same name (if there is one). If this option is checked the operation will be performed on the attribute which is in the same position in the dataset.

Values

The possible values for the selected attribute are displayed. No values are displayed for ordered variables.

Missing management - value for missing

The value that should be inserted if a task requires a value and no value is present. If no value is entered here the default value is used, i.e. the average for ordered attributes and the mode for nominal attributes. This is necessary, as some algorithms implemented in Rulex cannot manage missing values in the dataset.

Clicking Impute fills all the empty cells with the specified value.

Normalization

Select the method you want to use to normalize variables.

Possible methods are:

• None: no normalization is performed (default)

• Normal: data are normalized according to the Gaussian distribution, where μ is the average of x and σ is its standard deviation: 
  

  

• Minmax [0,1]: data are normalized to be comprised in the range [0,1]:
  

  

• Minmax [-1, 1]: data are normalized to be included in the range [-1, 1]:
  

  

Distance

Select the method you want to use to compute the distance between two patterns as regards the select attribute.

The distance between two examples is computed as the combination of the distances for each attribute. Possible choices are: Euclidean, Euclidean (normalized), Manhattan, Manhattan (normalized) and Pearson.

Suppose that there are n_E attributes for which Euclidean is to be used (normalized or not), n_M attributes for which Manhattan distance is to be used (normalized or not) and n_P attributes for which the Pearson distance is to be used.

Let's denote with x_E, x_M, x_P the subsets of x containing the values associated with the attributes where Euclidean, Manhattan and Pearson distance is to be used, respectively. If the normalized distance is to be used, suppose that the values in x_E, x_M, are already normalized (see the section on Normalization for further details). 

Then the distance between two patterns x and y can be defined as:

Where the distances are defined as follows:

Where:

• x_i is the value associated with the i-th variables in x

• is the average of the values in x

Minimum, Maximum, Average and Standard Deviation

Specify values used in the above formulas:

• minimum (min(x)) 

• maximum (max(x))

• average (μ)

• standard deviation (σ)

In the formulas regarding distance and normalization the average, standard deviation etc. are computed on the training set, but if required different values can be specified here.