Documentation » Historique » Version 11
François Rioult, 23/06/2010 18:08
1 | 3 | François Rioult | h1. Documentation |
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2 | 1 | François Rioult | |
3 | 4 | François Rioult | KDAriane is a set of operators for data mining and machine learning, and a set of scenarios (supervised classification, missing values completion, strong emerging pattern mining, etc.). It uses Ariane as a graphical platform for designing the data streams. |
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5 | 1 | François Rioult | h2. Installation |
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7 | As KDAriane requires Ariane, that requires Pandore, all have to be installed in the following order: |
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8 | 1 | François Rioult | * [[Pandore]] |
9 | 4 | François Rioult | * [[Ariane]] |
10 | * [[KDAriane]] |
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12 | 5 | François Rioult | h2. Special operators for shell scripting |
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14 | 5 | François Rioult | KDAriane is provided with basic components for executing shell scripts. The choice depends on how many parameters (p), input (i) and output (o) you want. The operators are named |
15 | @"eval" + p + i + o @ and call the eponymous .sh script. |
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17 | 6 | François Rioult | When an operator is executed, Ariane launches the script (for example @script.sh@) associated to the operator with giving the following arguments: |
18 | <pre> |
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19 | script.sh parameter-1 parameter-2 ... parameter-p input-1 input-2 ... input-i output-1 output-2 ... output-o |
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20 | </pre> |
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21 | 5 | François Rioult | |
22 | In Ariane, every operator has a return value, even if it has no output. |
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24 | 9 | François Rioult | The operator are divided in two categories: |
25 | * [[KDD operators]] are special components for calling Weka components, RapidMiner processes or MVminer binaries. |
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26 | * [[Shell operators]] that directly execute the commands entered by Ariane. |
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29 | 1 | François Rioult | h2. Scenarios |
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31 | KDAriane provides some examples of KDD realized through Ariane: |
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32 | 11 | François Rioult | * [[Data preparation]] : a first scenario for the binarization of CSV data. |
33 | 10 | François Rioult | * pattern mining and complexity visualization |
34 | * supervised classification with association rules |
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35 | * experiences about perturbation on training and test file with Weka classifiers and RapidMiner processes. |