R code for the paper "Temporal Exceptional Model Mining using Dynamic Bayesian Networks", published at the AALTD workshop of ECML 2020. For more details, see: https://project.inria.fr/aaltd20/files/2020/08/AALTD_20_paper_Bueno.pdf
Simulated data are provided separately in case you want to use it with other algorithms. For using the TEMM algorithm, no data need to be downloaded as the script will automatically generate the data.
Two steps are needed to run simulations.
- Learn the exceptional subgroups from simulated data
Type in the R console:
source("sd_simuldata.R")
This will generate the data based on several parameters, see sd_simuldata.R for more info. The script creates several files in your computer containing the discovered subgroups (R format).
- Compute classification measures (AUROC, precision, recall, etc)
The subgroups found will now be evaluated. Type in the console:
source("sd_simul_processresults.R")
Note that the parameters you set in the learning phase are required in this script as well.
The final results will be written to disk in two csv files, one for the unitary subgroups and another one for the specialized subgroups. Examples:
results-dsimul-unitary-ndynamic17-numseq10.csv
results-dsimul-specialized-ndynamic17-numseq10.csv