The `lbdc` package on PyPI is designed for fast and efficient DBSCAN clustering, often used for finding clusters in large datasets. To use the package, follow these steps:
1. **Installation:**
First, install the package using pip:
```bash
pip install lbdc
```
2. **Basic Usage:**
Here's a simple guide on how to use it after installation.
```python
import numpy as np
from lbdc import LBDC
# Example: Random data points
X = np.random.randn(100, 2) # 100 data points in 2D
# Initialize the LBDC model
model = LBDC(eps=0.5, min_samples=5) # You can adjust eps and min_samples based on your data
# Fit the model to the data
model.fit(X)
# Get the labels for each point
labels = model.labels_
print("Cluster labels:", labels)
```
3. **Explanation of parameters:**
- `eps`: The maximum distance between two points to be considered as in the same neighborhood.
- `min_samples`: The number of points to form a dense region (i.e., core points).
- `fit()`: Fits the DBSCAN model to your data.
- `labels_`: After fitting, you can access the labels for each data point (which cluster it belongs to).
You can adjust the `eps` and `min_samples` parameters to better suit the density of your data. Check the [documentation on GitHub](https://github.com/FarAway6834/lbdc) for more advanced features and options.헛소리 ㅋㅋㅋ