Swarm Intelligence for GPs

We provide Python implementations of swarm-based optimization algorithms for mining gradual patterns. The algorithm implementations include:

• Genetic Algorithm
• Particle Swarm Optimization
• Wasp Swarm Optimization
• Pure Random Search
• Pure Local Search

Requirements:

You will be required to install the following Python dependencies:

install python (version => 3.6)

Usage:

Use it a command line program with the local package to mine gradual patterns:

For example, we executed the GA-GRAANK algorithm on a sample data-set

$python3 src/main.py -a 'ga' -f data/DATASET.csv

where you specify the input parameters as follows:

• algorithm - [required] select algorithm ga, pso, wso, prs, pls
• filename.csv - [required] a file in csv format
• minSup - [optional] minimum support default = 0.5

Output

1. Age
2. Salary
3. Cars
4. Expenses

File: ../data/DATASET.csv

Pattern : Support
[('2', '+'), ('4', '-')] : 0.6
[('1', '-'), ('2', '-')] : 0.6
[('1', '-'), ('4', '+')] : 1.0
[('1', '+'), ('2', '+'), ('4', '-')] : 0.6
[('1', '+'), ('4', '-')] : 1.0
[('2', '-'), ('4', '+')] : 0.6
[('1', '+'), ('2', '+')] : 0.6
[('1', '-'), ('2', '-'), ('4', '+')] : 0.6

0.08473014831542969 seconds

License:

• MIT

References

• Dickson Owuor, Anne Laurent, and Joseph Orero (2019). Mining Fuzzy-temporal Gradual Patterns. In the proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FuzzIEEE). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8858883.
• Owuor, D., Runkler T., Laurent A., Menya E., Orero J (2021), Ant Colony Optimization for Mining Gradual Patterns. International Journal of Machine Learning and Cybernetics.
• Anne Laurent, Marie-Jeanne Lesot, and Maria Rifqi. 2009. GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets. In Proceedings of the 8th International Conference on Flexible Query Answering Systems (FQAS '09). Springer-Verlag, Berlin, Heidelberg, 382-393.