Clustering Model for ordinal data
At the end of this project, we will have
- Faster execution time of this code
- User friendly R Package
- Shiny Interface
I would be working on the authors the implementation of clustering model for categorical data. Summary of their work presented below.
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.
The implementation language will be R. Core parts of the work will be done in R (including RcppAmardillo and Shiny). Benchmark analysis will be done using different datasets. Documentation and examples will be added to the user guide and the API.
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
- Buffer time
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
- Name: Sai Krishna Kalyan
- Email: krishnakalyan3@gmail.com
- 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)
Hello, I am a second year Master student studying Masters (Data Mining and Knowledge Management) from University Lyon 2, France. I am completely available during the summer for 40 hours/week work.
Currently I am doing research internship at University Laval Quebec, Canada on training "Mixture of Experts using Neural Networks". I have made opens source contributions in projects like Spark and Scikit-Learn.
I would also like to you assure that I will give minimum 40 hours per week for my work. The only obligation I have currently is my Master Thesis and do not have any coursework pending.
I am quite organised with work and have weekly calendar with clear deadlines. Incase of difficulties in meeting the deadline, I will make sure that I inform my mentors / supervisor ahead of time.