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Clustering Model for ordinal data
- Name: Sai Krishna Kalyan
- Email: krishnakalyan3@gmail.com
- Telephone: +1-581-681-3826
- Github: krishnakalyan3
- University: Lumière University Lyon 2
- Course: Masters in Data Mining and Knowledge Management
- Expected Graduation date: June 2017
- Time Zone: EDT (GMT -04:00)
At the end of this project, we will have
- Faster execution time of this code
- User friendly R Package
- Shiny Interface
A model-based co-clustering algorithm for ordinal data is presented. This algorithm relies on the latent block model embedding a probability distribution specific to ordinal data (the so-called BOS or Binary Ordinal Search distribution). Model inference relies on a Stochastic EM algorithm coupled with a Gibbs sampler, and the ICL-BIC criterion is used for selecting the number of co-clusters (or blocks). The main advantage of this ordinal dedicated co-clustering model is its parsimony, the interpretability of the co-cluster parameters (mode, precision) and the possibility to take into account missing data. Numerical experiments on simulated data show the efficiency of the inference strategy, and real data analyses illustrate the interest of the proposed procedure. (https://hal.inria.fr/hal-01448299)
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.
- Experiment with RcppAmardillo
- Benchmarking existing code with different data sets
- Refactoring code with RcppAmardillo
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.
- API documentation
- Write Examples
- Create a user friendly R package
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.
- Design UI
- Server implementation
- Deploy Application to server
Please get in touch with Julien JACQUES and Christophe BIERNACKI for this project.
[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