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.

Demonstration¶

You can watch the short demonstration of KDAriane.

Installation¶

• Prerequisite
• KDAriane

Tutorial¶

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

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]

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 for a full documentation about Ariane.

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

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

See Eval operators for shell scripting or calling binaries.

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.

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

Routines¶

Routines are complex streams that Ariane allows you to share.

• shell routines
• routines for missing values
• KDD routines

To-do¶

• sous-projet members
• unifier options mvminer

Appendix¶

Utilities¶

• gnuplot: plotting data with GNUplot
• score : computing validation values from a decision file
• complement :
• dictionary :
• segmentation :

References¶