Clustering Model for ordinal data

Summary

Model-based clustering and co-clustering algorithm for ordinal data are provided. These algorithms relie on the BOS model (Biernacki and Jacques, 2015, Stat. Comput.) for ordinal data and a SEM-Gibbs algorithm for inference. (https://hal.inria.fr/hal-01448299)

Description

Related work and impact

• Shiny Web Interface
• Optimise execution time by refactoring code using RcppAmardillo package
• Create a user friendly R package.

Project Milestones

Phase 1

Optimise execution time by refactoring code using RcppAmardillo package. For this, a preliminary phase of tests should find the most computationally heavy part of the inference algorithm.

Phase 2

Compile the code in order to create a R package. The package should be easy of use for non specialists, fast, and provide useful output and graphical representations of the results.

Final

The results should be presented through a Shiny interface, in which the user can move into the solution space by changing the number of clusters.

Mentors

Please get in touch with Julien JACQUES and Christophe BIERNACKI for this project.

Tests

References

[1] C. Biernacki and J. Jacques (2016), Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm, Statistics and Computing, 26 [5], 929-943 [2] J.Jacques and C.Biernacki (2017), Model-based co-clustering for ordinal data, Preprint HAL n°01448299