https://github.com/parvvaresh/clustering-with-genetic
This Python script implements a genetic algorithm for clustering data. The algorithm optimizes the cluster assignments of data points using a genetic approach, aiming to improve the silhouette score. The silhouette score is a measure of how well-defined the clusters are in the data.
https://github.com/parvvaresh/clustering-with-genetic
clustering genetic-algorithm python3
Last synced: about 1 year ago
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This Python script implements a genetic algorithm for clustering data. The algorithm optimizes the cluster assignments of data points using a genetic approach, aiming to improve the silhouette score. The silhouette score is a measure of how well-defined the clusters are in the data.
- Host: GitHub
- URL: https://github.com/parvvaresh/clustering-with-genetic
- Owner: parvvaresh
- Created: 2024-01-01T20:49:57.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-03T09:02:59.000Z (over 1 year ago)
- Last Synced: 2025-03-25T04:03:43.974Z (over 1 year ago)
- Topics: clustering, genetic-algorithm, python3
- Language: Python
- Homepage:
- Size: 160 KB
- Stars: 10
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Genetic Clustering Algorithm
This Python script implements a genetic algorithm for clustering data. The algorithm optimizes the cluster assignments of data points using a genetic approach, aiming to improve the silhouette score. The silhouette score is a measure of how well-defined the clusters are in the data.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Algorithm Overview](#algorithm-overview)
- [Genetic Class](#genetic-class)
- [Cluster Class](#cluster-class)
- [Parameters](#parameters)
- [Results](#results)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## Installation
```bash
pip install cluster_ga
```
## Usage
```python
from sklearn import datasets
import numpy as np
import pandas as pd
from cluster_ga.cluster import cluster
# this is a for test
iris = datasets.load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
x = np.array(iris_df[["petal length (cm)", "petal width (cm)"]])
y = iris.target
# Instantiate and fit the model
model = cluster(x, y, 500, 0.9,150)
model.fit()
# show fitness plot
model.show_plot()
```
## Algorithm Overview
The genetic clustering algorithm consists of the following components:
### Genetic Class
Defines the genetic operations such as mutation, generation, and fitness calculation.
### Cluster Class
Manages the clustering process, including the initialization of populations, evolution, and convergence.
## Parameters
- `size_population`: Number of individuals in the population.
- `goal`: The desired fitness score to achieve.
- `repeat`: Number of generations to run the algorithm.
- `is_mutation`: Boolean flag to enable or disable mutation.
## Results
The script outputs the progress of the algorithm, including the generation number and the fitness score achieved. Additionally, a plot of the fitness scores over generations is displayed at the end of the execution.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
## Acknowledgments
- This implementation is inspired by genetic algorithms and clustering techniques.
- Special thanks to the scikit-learn library for providing the silhouette score metric.