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49 Workflows found for "kind:(RapidMiner)".

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Workflow Image Mining with RapidMiner (v1)

Created: 28/04/10 @ 11:00:37 | Last updated: 28/04/10 @ 11:01:04

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This is an image mining process using the image mining Web service provided by NHRF within e-Lico. It first uploads a set of images found in a directory, then preprocesses the images and visualizes the result. Furthermore, references to the uploaded images are stored in the local RapidMiner repository so they can later be used for further processing without uploading images a second time.

Rating: 0.0 / 5 (0 ratings) | Versions: 1 | Reviews: 0 | Comments: 1 | Citations: 0

Viewed: 183 times | Downloaded: 90 times

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Original Uploader

Workflow Looping over Examples for doing de-aggregation (v1)

Created: 29/04/10 @ 16:21:56

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This process is based on (artificially generated) data that looks like it has been aggregated before. The integer attribute Qty specifies the quantity of the given item that is represented by the rest of the example. The process now loops over every example and performs on each example another loop, that will append the current example to a new example set. This example set has been created as empty copy of the original example set, so that the attributes are equally. To get access to and rem...

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Viewed: 49 times | Downloaded: 30 times

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Workflow Using Remember / Recall for "tunneling" results (v1)

Created: 29/04/10 @ 16:07:55

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This process shows how Remeber and Recall operators can be used for passing results from one position to another position in the process, when it's impossible to make a direct connection. This process introduces another advanced RapidMiner technique: The macro handling. We have used the predefined macro a, accessed by %{a}, that gives the apply count of the operator. So we are remembering each application of the models that are generated in the learning subprocess of the Split validation. Af...

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Viewed: 32 times | Downloaded: 10 times

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Original Uploader

Workflow Crossvalidation with SVM (v1)

Created: 29/04/10 @ 16:42:49

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
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.

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Viewed: 73 times | Downloaded: 31 times

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Original Uploader

Workflow Stacking (v1)

Created: 29/04/10 @ 16:53:35

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
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.

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Viewed: 69 times | Downloaded: 30 times

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Workflow Generating Example Weights depending on label (v1)

Created: 03/05/10 @ 09:47:42

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This process uses a Generate Attribute operator for assigning new weights to examples. It uses the if condition of this operator to distinguish between labels. This can be especially useful if you have a strong bias in your class frequency, which can harm learning. Please note that you must use a learning algorithm that supports weighted examples.

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Viewed: 42 times | Downloaded: 19 times

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Workflow Transaction Analysis Demo from RM 5 Intro Day (v1)

Created: 30/04/10 @ 08:19:39 | Last updated: 05/05/10 @ 09:58:51

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
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...

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Viewed: 123 times | Downloaded: 66 times

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Workflow Weighted Score Tables (v1)

Created: 05/05/10 @ 14:58:29

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This process calculates a weighted score as known from SAP BW. The first operator generates data similar to that used in the links you provided. The next operator (Discretize) defines the different age groups. The next three operators map each age group to the value used for scoring. The last operator finally calculates the score and adds it as a new column to your data set.

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Original Uploader

Workflow test (v1)

Created: 10/05/10 @ 17:04:57

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
<table><tr><td><p>This process starts with loading the data. After finishing the input operator a typical learning step is performed. Here, an implementation of a decision tree learner is used which also can handle numerical values (similar to the well known C4.5 algorithm).</p></td><td><icon>groups/24/learner</icon></td></tr></table><p>Each operator may demand some input and delivers some output. These in- and outp...

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Viewed: 18 times | Downloaded: 1 time

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Workflow Defining positive class with Remap Binominal (v1)

Created: 11/05/10 @ 09:04:30

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

Preview
This process shows how one can use the Remap Binominal operator to define which label value is treated as the positive class.

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Viewed: 17 times | Downloaded: 9 times

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