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Tag Clustering (TaCl) (1)

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This is a sample process for a tag clustering. See http://www-ai.cs.uni-dortmund.de/SOFTWARE/TaCl/index.html

Created: 2011-11-17 | Last updated: 2011-11-17

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RapidAnalytics Video Series Demo Processes

Created: 2011-11-02 15:02:21 | Last updated: 2011-11-02 18:00:41

3 items in this pack

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Mining Semantic Web data using Corresponde... (1)

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This workflow will explain that how an example set can be extracted from an RDF resource using the provided SPARQL query. This example set is then divided into training and test parts. These sub-example sets are used by the Correspondencce Analysis operators (encapsulate the Correspondencce Analysis data transformation technique) which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve clas...

Created: 2011-06-25 | Last updated: 2011-06-25

Mining Semantic Web data using Corresponde... (1)

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This workflow describes how to learn from the Semantic Web's data using a data transformation algorithm 'Correspondence Analysis'. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is divided into training and test parts. These sub-example sets are used by the Correspondence Analysis operators (encapsulate the Correspondence Analysis data transformation technique) which processes each feature at a time and transform the data into a different...

Created: 2011-06-25 | Last updated: 2011-06-25

Mining Semantic Web data using FastMap - E... (1)

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This workflow describes how to learn from the Semantic Web's data. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is then divided into training and test parts. These sub-example sets are used by the FastMap operators (encapsulate the FastMap data transformation technique), which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve classfica...

Created: 2011-06-25 | Last updated: 2011-06-25

SVD user-based collaborative filtering rec... (1)

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This workflow takes user-item matrix A as a input. Then it calculates reduced SVD decomposition A_k by taking only k greatest singular values and corresponding singular vectors. This worfkflow calculates recommendations and predictions for particular user %{id} from matrix A. Particular row %{id} is taken from original matrix A and replaced with %{id} row in A_k matrix. Predictions are made for %{id} user based on another users A_k. Note: This workflow uses R-script operator with R library ...

Created: 2011-05-09 | Last updated: 2011-05-09

Credits: Ninoaf Matko Bošnjak

Attributions: User-based collaborative filtering recommender system template Datasets for the pack: RCOMM2011 recommender systems workflow templates

LSI content based recommender system template (1)

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This workflow performs LSI text-mining content based recommendation. We use SVD to capture latent semantics between items and words and to obtain low-dimensional representation of items. Latent Semantic Indexing (LSI) takes k greatest singular values and left and right singular vectors to obtain matrix  A_k=U_k * S_k * V_k^T. Items are represented as word-vectors in the original space, where each row in matrix A represents word-vector of particular item. Matrix U_k, on the other hand ...

Created: 2011-05-06 | Last updated: 2011-05-09

Credits: Ninoaf Matko Bošnjak

Attributions: Content based recommender system template Datasets for the pack: RCOMM2011 recommender systems workflow templates

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Datasets for the pack: RCOMM2011 recommender systems...

Created: 2011-05-05 21:18:51 | Last updated: 2011-05-06 12:13:22

Credits: Matko Bošnjak Ninoaf

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: ZIP archive

Comments: 0 | Viewed: 766 times | Downloaded: 448 times