### Machine Learning

See also

Course material,

Other LABS,

Course projects
#### LABS

**3. Unsupervized Learning**
Context

Consider as an example data consisting of the composition of 45 pottery found in Britain dating back to Roman time. The composition is the content of 9 components (iron, manganese ....). They come from 5 different ovens. The data matrix is the 45 × 9 matrix of the compositions.

Goal

Use the Kmeans and Gaussian mixture algorihms to cluster the poteries dataset.

Example : examples of codes are provided for the digits data.

Questions

1. Run the Kmeans algorithm, compare the obtained classificaiton to the four labels using a confusion matrix

2. Run the GMM algorithm , compare the obtained classificaiton to the four labels using a confusion matrix

3. Compare the clustering results to each other (compute the number of well classified examples).

Codes:

Poteries_clustering_ToStart.py,

Poteries_clustering_ToStart.py,

Digits_clustering.py,

Digits_clustering.R