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RCOMM Challenge 2: Broken Iris (Preparation) (1)

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This workflow creates the input for the RCOMM 2010 Challenge 2. The solution and description are in workflow "RCOMM Challenge 2: Broken Iris"

Created: 2010-09-17

RCOMM Challenge 1: 99 bottles of beer (1)

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At the RComm 2010 (www.rcomm2010.org), an unusual competition was held. Titled "Who Wants to Be a Data Miner", three challenges were issued to the participants of the conference. In all challenges, participants had to design RapidMiner processes as quickly as possible. This is the winning process of Challenge 1: "99 bottles of beer" by Sebastian Land. This was the task: Design a process that produces an example set the rows of which form the lyrics of the well-known song "99 bottles of beer...

Created: 2010-09-17

Cross tabulation via aggregation and pivoting (1)

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Creates a contingency table using the Aggregate and Pivot operators.

Created: 2010-08-25 | Last updated: 2010-08-25

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

Transaction Analysis Demo from RM 5 Intro Day (1)

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This is the demo process presented at the RapidMiner 5 Intro Day. It combines customer segmentation with direct mailing. It loads some transaction data, aggregates and pivotes the data so it can be used by a clustering to perform a customer segmentation. Then, additional data is joined with the clustered data. First, response/no-response data is joined, and them some additional information about the users is added. Finally, customers are classified into response/no-response classes. The dat...

Created: 2010-04-30 | Last updated: 2010-05-05

Stacking (1)

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RapidMiner supports Meta Learning by embedding one or several basic learners as children into a parent meta learning operator. Here, we use a three base learners inside the stacking operator: decision tree induction, linear regression, and a nearest neighbours classifier. Finally, a Naive Bayes learner is used as a stacking learner which uses the predictions of the preceeding three learners to make a combined prediction.

Created: 2010-04-29

Crossvalidation with SVM (1)

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Performs a crossvalidation on a given data set with nominal label, using a Support Vector Machine as a learning algorithm. Inside the cross validation, the first subprocess generates an SVM model, and the second subprocess evaluates it. applying it on a so-far unused subset of the data and counting the misclassifications.

Created: 2010-04-29