KDAriane

h1. Documentation

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.

h2. Demonstration

You can watch the [[short demonstration of KDAriane]].

h2. Installation

* [[Prerequisite]]

* [[KDAriane]] 

h2. Tutorial

You can read the document [[Data preparation]] for a first contact with @KDAriane@.

h2. Scenarios

In the @sample@ folder, @KDAriane@ provides some examples of KDD scenarios:

* [[Data preparation]]: a first scenario for the binarization of CSV data.

* [[Data mining complexity]]: pattern mining and complexity visualization

* [[CMAR]]: supervised classification with association rules [1]

* [[CAEP]]: supervised classification with emerging patterns [2]

* experiences about perturbation on training and test file with Weka classifiers and RapidMiner processes.

h2. How is Ariane working?

Ariane is a graphical platform for designing image processing streams. Ariane works with graphical operators, and allows to build loops and while.

"Go to the Aiane dedicated site":http://www.greyc.ensicaen.fr/~regis/Ariane/ for a full documentation about Ariane.

h2. How is KDAriane working?

KDAriane is provided with basic components for executing shell scripts. When an operator is executed, Ariane launches the script with giving the following arguments:

<pre>

script.sh parameter-1 parameter-2 ... parameter-p input-1 input-2 ... input-i output-1 output-2 ... output-o

</pre>

See [[Eval operators]] for shell scripting or calling binaries.

h2. Operators

The operator are divided in three categories: 

* [[Eval operators]] that directly execute the commands entered by Ariane. 

* [[KDD operators]] are special components for calling Weka components or RapidMiner processes.

* [[Shell operators]] that directly execute the commands entered by Ariane. 

h2. Pattern mining prototypes

KDAriane uses some specific prototypes: 

* [[music-dfs]] : mining patterns under various constraints

* [[mtminer]] : levelwise minimal transversals of hypergraph

* [[mvminer]] : mining delta-free patterns in presence of missing values

h2. Routines

Routines are complex streams that Ariane allows you to share.

* [[shell routines]]

* [[routines for missing values ]]

* [[KDD routines]]

h2. Appendix

h3. Utilities

* [[gnuplot]]: plotting data with GNUplot

* [[score]] :

* [[complement]] :

* [[dictionary]] :

* [[segmentation]] :

h3. References