sklearn and pyclustering style implementations of SFLA and ABC
Project description
Metaheuristic Clustering
As the name suggests, this is a repository for metaheuristic clustering algorithms, implemented in Python 3, that I could not find implemented elsewhere.
Implementations are designed to work with or without the sklearn implementation style.
Currently the algorithms implemented are:
- Artifical Bee Colony (ABC)
- Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied soft computing
- Shuffled Frog Leaping Algorithm (SFLA)
- Amiri, B., Fathian, M., & Maroosi, A. (2009). Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology, 45(1), 199-209.
Dependencies
scikit-learn - only needed for interop with scikit-learn
Example
Sklearn/Object style
data = X # your data
# SFLA Clustering
from src.metaheuristic_clustering.sfla import SFLAClustering
sfla_model = SFLAClustering()
sfla_labels = sfla_model.fit_predict(data)
# ABC Clustering
from src.metaheuristic_clustering.abc import ABCClustering
abc_model = ABCClustering()
abc_labels = abc_model.fit_predict(data)
Function style
import src.metaheuristic_clustering.util as util
data = X # your data
# SFLA Clustering
import src.metaheuristic_clustering.sfla as sfla
best_frog = sfla.sfla(data)
sfla_labels = util.get_labels(data, best_frog)
# ABC Clustering
import src.metaheuristic_clustering.abc as abc
best_bee = abc.abc(data)
abc_labels = util.get_labels(data, best_bee)
Sample Results
ABC
SFLA
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