myExperiment - Groups

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Lawrynka

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RMonto pack

Created: 2012-01-29 09:47:09 | Last updated: 2012-03-05 22:24:02

RMonto is an ontological extension to RapidMiner, that provides possibility of machine learning with formal ontologies. RMonto is an easily extendable framework, currently providing support for unsupervised clustering with kernel methods and (frequent) pattern mining in knowledge bases. One important feature of RMonto is that it enables working directly on structured, relational data. Additionally, its custom algorithm implementations may be combined with the power of RapidMiner thr...

9 items in this pack

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RapidMiner

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Lawrynka

Semantic clustering (with AHC) of SPARQL q... (1)

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The workflow uses RapidMiner extension named RMonto (http://semantic.cs.put.poznan.pl/RMonto/) to perform clustering of SPARQL query results based on chosen semantic similarity measure. The measure used in this particualr workflow is a kernel that exploits membership of clustered individuals to OWL classes from a background ontology ("Common classes" kernel from [1]). Since the semantics of the backgound ontology is used in this way, we use the name "semantic clustering". ...

Created: 2012-01-29 | Last updated: 2012-01-29

RapidMiner

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Lawrynka

Semantic clustering (with k-medoids) of SP... (1)

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The workflow uses RapidMiner extension named RMonto (http://semantic.cs.put.poznan.pl/RMonto/) to perform clustering of SPARQL query results based on chosen semantic similarity measure. Since the semantics of the backgound ontology is used in this way, we use the name "semantic clustering". The SPARQL query is entered in a parameter of "SPARQL selector" operator. The clustering operator (k-medoids) allows to specify which of the query variables are to be used as clustering criteria. If more ...

Created: 2012-01-29

Taverna 2

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Rishi Ramgolam

Discretize by Binning (1)

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Choosing an attribute ('a1') with an attribute filter type condition (single).

Created: 2011-12-20 | Last updated: 2011-12-20

Credits: Rishi Ramgolam

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James Eales

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Creating a focused corpus of factual outcomes from b...

Created: 2011-06-28 11:19:04 | Last updated: 2011-12-13 16:02:16

This pack contains resources and supplementary files for the submission to the MIND2011 workshop titled "Creating a focused corpus of factual outcomes from biomedical experiments" by James Eales, George Demetriou and Robert Stevens

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James Eales

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RapidMiner plugin for Taverna videos and descriptions

Created: 2011-06-06 10:17:52 | Last updated: 2011-12-13 16:02:04

This pack contains videos the show how to use various parts of the RapidMiner plugin for Taverna. The videos demonstrate how to build a Taverna workflow which collects a GEO dataset, uploads it to RapidAnalytics, trains a classifier on one half of the data and tests it on the other half. This classification process can be used to gauge how well mutant and control assays agree across experimental repeats.

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assays
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e-lico
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elico
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geo
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rapidminer
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svm

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Adam Woznica

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Data mining on KUP data

Created: 2011-05-23 12:35:37 | Last updated: 2011-05-24 12:31:58

Various Rapid Miner workflows and R scripts that analyse and visualize KUP-related data extracted from the KUP knowledge base.

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Taverna 2

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James Eales

One sentence per line (1)

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This workflow accepts a plain text input and provides a single text document per input containing one sentence per line. Newline characters are removed from the original input. The OpenNLP sentence splitter is used to split the text, this is provided by University of Manchester Web Services.