Coclustering
http://www.hds.utc.fr/
2024-03-29T00:31:53+01:00Coclustering
http://www.hds.utc.fr/
http://www.hds.utc.fr/lib/tpl/heudiasyc/images/favicon.icotext/html2013-01-30T17:57:20+01:00lometauren:description
http://www.hds.utc.fr/doku.php?id=en:description&rev=1359565040&do=diff
Data Sets
Each data set consists in four components
* a data table whose rows/columns have to be clustered,
* a set of possible row labels,
* a set of possible columns labels,
* a set of parameters describing the generative model employed.text/html2013-01-30T17:47:04+01:00lometauren:downloads
http://www.hds.utc.fr/doku.php?id=en:downloads&rev=1359564424&do=diff
Full Repository
Data type File Binary [Download full repository] Contingency [Download full repository] Gaussian [Download full repository]
Binary Data Sets
Error rate Table Size Number of Classes File 5% 50×50 3×3 [Download] 5% 50×50 5×5 [Download] 5% 50×50 10×10 [Download] 5% 100×100 3×3text/html2012-04-02T19:48:45+01:00lometauren:citation
http://www.hds.utc.fr/doku.php?id=en:citation&rev=1333388925&do=diff
The motivations for creating this repository, the precise definitions of all its ingredients, and the description of the generation process are described in the following technical report.
Lomet, A., Govaert, G. and Grandvalet, Y. (2012)
Design of Artificial Data Tables for Co-Clustering Analysis.
Technical report, Université de Technologie de Compiègne, France.text/html2012-04-02T19:48:17+01:00lometauren:start
http://www.hds.utc.fr/doku.php?id=en:start&rev=1333388897&do=diff
[ Summary Table]
[ Unsorted Table]
[ 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: