{{ en:data_center_err_05_n_50_g_3.png?210| Summary Table}}
{{en:data_err_05_n_50_g_3.png?210 | Unsorted Table}}
{{ en:data_org_err_05_n_50_g_3.png?210 | Sorted Table}}

This repository aims at sharing data sets for the analysis of co-clustering algorithms. 
It currently contains 72 artificial data tables of reals, which have been generated from latent block models.
The main benefits of using these data sets are that:
  - the true generative model is known;
  - several plausible ground truths are provided for labeling;
  - the ultimate classification error is carefully controlled;
  - the magnitude of row and column classification errors are similar. 
Item 1. ensures that the co-clustering structure exists; items 2. and 3. enable the accurate absolute and relative assessments of the benchmarked co-clustering compounds; item 4. ensures that the learning problem indeed belongs to the co-clustering problems category, whose analysis cannot be conducted by one-way clustering tools.
A more comprehensive description of the data sets is given [[en:description|here]], and supporting evidences regarding the claims above are detailed {{en:design_data_coclustering.pdf|there}}.

For information about citing data sets in publications, please see [[en:citation|here]]. 

If you have comments, suggestions, if you wish to donate a series of data sets, or for any other question, feel free to contact the repository [[coclustering@hds.utc.fr|maintainer]].