A simple Python package for one-dimensional data clustering, implementing various clustering algorithms including traditional and novel approaches.
Install the package using pip:
pip install usmerge
This package provides multiple one-dimensional clustering methods:
- Equal Width Binning (equal_wid_merge)
- Equal Frequency Binning (equal_fre_merge)
- K-means Clustering (kmeans_merge)
- SOM-K Clustering (som_k_merge)
- Fuzzy C-Means (fcm_merge)
- Kernel Density Based (kernel_density_merge)
- Information Theoretic (information_merge)
- Gaussian Mixture (gaussian_mixture_merge)
- Hierarchical Density (hierarchical_density_merge)
- Jenks Natural Breaks (jenks_breaks_merge)
- Quantile-based (quantile_merge)
- DBSCAN (dbscan_1d_merge)
The package accepts various input formats:
- pandas Series/DataFrame
- numpy array
- Python list/tuple
- Any iterable of numbers
- Equal Width Binning:
from usmerge import equal_wid_merge
labels, edges = equal_wid_merge(data, n=3)- Equal Frequency Binning:
from usmerge import equal_fre_merge
labels, edges = equal_fre_merge(data, n=3)- K-means Clustering:
from usmerge import kmeans_merge
labels, edges = kmeans_merge(data, n=3, max_iter=100)- SOM-K Clustering:
from usmerge import som_k_merge
labels, edges = som_k_merge(data, n=3, sigma=0.5, learning_rate=0.5, epochs=1000)- Fuzzy C-Means:
from usmerge import fcm_merge
labels, edges = fcm_merge(data, n=3, m=2.0, max_iter=100, epsilon=1e-6)- Kernel Density Based:
from usmerge import kernel_density_merge
labels, edges = kernel_density_merge(data, n=3, bandwidth=None)- Jenks Natural Breaks:
from usmerge import jenks_breaks_merge
labels, edges = jenks_breaks_merge(data, n=3)- Quantile-based Clustering:
from usmerge import quantile_merge
labels, edges = quantile_merge(data, n=3)- DBSCAN Clustering:
from usmerge import dbscan_1d_merge
labels, edges = dbscan_1d_merge(data, n=3, min_samples=3)All clustering methods return two values:
- labels: List of cluster labels for each data point
- edges: List of cluster boundaries
import numpy as np
import matplotlib.pyplot as plt
from usmerge import som_k_merge, fcm_merge, kmeans_merge, hierarchical_density_merge, dbscan_1d_merge
# Generate synthetic data with three clear clusters
np.random.seed(42)
data = np.concatenate([
np.random.normal(0, 0.3, 50), # First cluster
np.random.normal(5, 0.4, 50), # Second cluster
np.random.normal(10, 0.3, 50) # Third cluster
])
# Compare different clustering methods
methods = {
'SOM-K': som_k_merge(data, n=3, sigma=0.5, learning_rate=0.5, epochs=1000),
'FCM': fcm_merge(data, n=3, m=2.0, max_iter=100),
'K-means': kmeans_merge(data, n=3),
'DBSCAN': dbscan_1d_merge(data, n=3, min_samples=3),
'Hierarchical Density': hierarchical_density_merge(data, n=3)
}
# Visualize results
plt.figure(figsize=(15, 5))
for i, (name, (labels, edges)) in enumerate(methods.items(), 1):
plt.subplot(1, 5, i)
plt.scatter(data, np.zeros_like(data), c=labels, cmap='viridis')
plt.title(f'{name} Clustering')
# Plot cluster boundaries
for edge in edges:
plt.axvline(x=edge, color='r', linestyle='--', alpha=0.5)
plt.ylim(-0.5, 0.5)
plt.tight_layout()
plt.show()Each clustering method has its own set of parameters:
- SOM-K:
sigma(neighborhood size),learning_rate(learning rate),epochs(iterations) - FCM:
m(fuzziness),max_iter,epsilon(convergence threshold) - Kernel Density:
bandwidth(kernel width) - Information Theoretic:
alpha(compression-accuracy trade-off) - Gaussian Mixture:
max_iter,epsilon(convergence threshold) - Hierarchical Density:
min_cluster_size(minimum points per cluster) - Jenks Natural Breaks: Only requires number of clusters
- Quantile-based: Only requires number of clusters
- DBSCAN:
n(target number of clusters),eps(optional neighborhood size),min_samples(minimum points in cluster),max_iter(maximum iterations for eps adjustment)
Feel free to contribute to this project by submitting issues or pull requests.
MIT License