Evaluation of an approximate approach on synthetic and medical data.
The code needs Python 3.0 in order to be run.
main.py- It manages the three algorithms to assess the Sequential Pattern Mining problem.
- It contains an helper that provide all the information to run the code.
generator/dataset_generator.py- It contains a simple code to generate a random dataset of sequences of complex structure.
- There is the gen.sh to generate automatically a set of datasets throught this script
- print_graphs.py -> Support program to generate graphics on results
- comparator.py -> Support program to compare .db outputs
The directory is structured as follow:
generator:- It contains the script used to generate the synthetic datasets.
datasets:- It contains the input datasets, generated or not by the dataset_generator
- if generated, the name contains the information about the setting used
experiments:- It contains the csv output data of the main program
- Each row contains specific information about a single algorithm execution
results:- It contains the .db output data of the main program
- Each file contains the frequent sequences the main program found
- The name define the algorithm used and information to recover the input data
data_printers:- It contains some helper scripts used to generate the graphs.