# Visual Analytics Course

code_examples

## Visual Analytics Course

### Code examples

This page gathers code snippets (python classes) that are meant to be used within the Tulip environment.

The examples are far from being perfect, and are only meant to illustrate the ideas and concepts that are described in the corresponding lessons or pages in this wiki. The code is not robust, methods must be called and used with precautions. Please feel free to send a note and warn us about incoherences or mistakes.

#### High-dimensional data

• PCA: this class implements the usual PCA method
• GraphPCA: this class is a utility class to the PCA class. You give it a graph and a list of properties so you can use the PCA class within Tulip. You actually do not need a graph to use it, just a set of nodes (points) with a bunch of metrics.
• MDS: this class implements the matrix approach to computing the MDS projection.
• GraphMDS: this class is a utility class to the MDS class. You give it a graph and a list of properties so you can use the MDS class within Tulip. You actually do not need a graph to use it, just a set of nodes (points) with a bunch of metrics.

#### Clustering & Aggregation

• k-Means: this class implements a limited form of the usual k-means method. You give it a graph and it will compute clusters based on node positions.
• Louvain: this class implements the Louvain clustering algorithm for a graph. It assigns nodes an integer value. Subgraphs, and the resulting quotient graph, can then be computed by calling the appropriate EqualValue and Quotient Clustering plug-ins from within Tulip, for example.