Workflows

Creation of New Attribute Depending on Val... (1)

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The process shows the usage of the operator "Generate Attributes" in combination with an "if - then - else" condition and nominal values. The values "value0" and "value1" are mapped to "T1", other values are mapped to "T2" for the new attribute.

Created: 2010-06-01

Association rules as examples (1)

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Uses Groovy scripting to lay out rules and their support metrics as examples.

Created: 2010-06-02

Combining nominal attributes with missing (1)

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This process will show how one can combine nominal values of attributes that contain missing values. A generate attribute operator is used and hence forbidden characters must first be replaced. After this, a condition in the generation ensures that no question mark (standing for missing value) will be shown in the result, if any of the two combined attributes is known.

Created: 2010-06-04

Prepending common prefix to attributes (1)

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This process shows how one can add a common prefix to a subset of attributes. First a subset might be defined by the attribute set selection parameters of the rename by replacing operator. Then one can make use of the capturing group functionality of regular expressions to insert the original name into the replacement.

Created: 2010-06-04

Transform Attribute Names to Upper Case (S... (1)

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This process uses a Script operator which transforms the attribute names of the input example set into UPPER case.

Created: 2010-06-06

Transform Attribute Names to lower Case (S... (1)

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This process uses a Script operator which transforms the attribute names of the input example set into lower case.

Created: 2010-06-06

Same Number of Examples per Class (Stratif... (1)

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This process can be used to sample examples from each class of the data set so that the number of examples per class is the same for all classes. The name of the label attribute is defined in the first "Set Macro" operator within the subprocess "Stratification". The result will be a stratified data set where each class is represented by the minimum number of examples for a single class minus 1 (due to calculation reasons in absolute sampling which is used here). The first two operators just...

Created: 2010-06-10

Function finding (1)

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This process recursively generates new attributes by mathematically combining existing attributes with user selectable operators. At the end of each pass through the recursion attributes are thinned to prevent silicon smoke and unpleasantness. Sample input. P1    P2    A1    A2    RESULT 2       3      4      5      15 6     &...

Created: 2010-06-12 | Last updated: 2010-06-29

Preprocessing nominal data for frequent it... (1)

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This process will first create artificial data that can be compared to usual data loaded for frequent item set mining: Nominal Data with a true and false value, but differently mapped to internal indices. For ItemSet Mining these must be preprocessed to avoid problems: First they have to be transformed to Binominal Attributes, then it has to be defined, which is the positive and the negative value.

Created: 2010-06-16 | Last updated: 2010-06-16

Optimizing Discretization (1)

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This process generates a decision tree on the Iris data set. Before the decision tree is generated, the input attributes are discretised so we only work on nominal attributes. We use a combination of "Select Subprocess" and "Optimize Parameters" to select the best out of five different discretizazion methods independently for each of the attributes. The process shows, that the resulting accuracy heavily depends on the choice of the method. It varies between 64% and 94%.

Created: 2010-06-18 | Last updated: 2010-06-18