Search results for "rapidminer"

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

Finding all Examples that have duplicate v... (1)

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This process will retrieve all examples, who have identical values in a specific attribute. For testing, the following data can be writen into the file, that will be read by the Read CSV operator: CID,Value 3596,X 4054,X 4054,X 3000,S 3000,T 3000,U 32135,S The target of this process is to return the two examples having the same value in the CID column. To achieve this, first a real id is generated by the generate id. After this, we have to find all duplicates: For this we first remove dupl...

Created: 2010-06-18

Discretization into Deviation Interval aro... (1)

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This process shows how numerical attributes can be discretized into intervals based on the standard deviation for each attribute around their mean values.

Created: 2010-06-22 | Last updated: 2010-06-22

Pre process data per group using recall re... (1)

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This process takes as input a dataset with different groups. For each group it discretises attributes into High, Medium, Low. This creates a group invariant representation.

Created: 2010-06-23 | Last updated: 2010-06-23

Macro Propagation into Subprocesses and Ex... (1)

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<p> This small process demonstrates how to propagate macros into a subprocess or Execute Process operator </p> <p> When macros are set once, they are available in subprocesses imediatly (see Generate Attributes in the seconde Subprocess operator) </p> <p> If someone wants to propagate a macro into a Excecute Process operator one need to edit the "macros" parameter in the Parameters list of the Execute Process operator. </p> Tags: Subprocess, Execute Proc...

Created: 2010-06-25

Holograph Reduced Representation (1)

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After Kanerwa

Created: 2010-06-29