This is the continuation of the experiments started few days ago.

Two other datasets have been involved in this type of experiments. Both of them are Astrophysics datasets, more precisely two dataset containing Stars and Galaxies.

Star/Galaxies separation is a problem usually tackled with supervised learning methodologies. In our work several clustering testes are conducted on such type of data.

These two datasets was chosen to be quite simple to separate, because we are interested in the robustness with respect missing values.

Starting from the original datasets, I have created eight variants for each of them, in this way

• 4 variants affecting only 3 features out of 15, with 5, 10, 20, 30 percent of objects reporting missing values for all of the 3 features, respectively
• 4 variants affecting 6 features out of 15, with 5, 10, 20, 30 percent of objects reporting missing values for all of the 6 features, respectively

The experiments was done with Euclidean Co-clustering (Information-theoretic cannot work with negative values) and SVC.

An archive with all results is available for download (it contains also the results of the previous stage).

In the files above:

- “MV� stands for “Missing Values�
- “FC� stands for “Feature Clusters�
- FC1 means no feature clustering
- FC2 means two clusters of feature requested
- FC3 means three clusters of feature requested
- CC stands for Co-clustering