usmerge 0.2.0

A simple package to merge one-dimension data by unsupervised method

Unsupervised Merge

A simple Python package for one-dimensional data clustering, implementing various clustering algorithms including traditional and novel approaches.

Installation

Install the package using pip:

pip install usmerge

Features

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)

Usage

Data Format

The package accepts various input formats:

• pandas Series/DataFrame
• numpy array
• Python list/tuple
• Any iterable of numbers

Basic Usage Examples

1. Equal Width Binning:

from usmerge import equal_wid_merge
labels, edges = equal_wid_merge(data, n=3)

2. Equal Frequency Binning:

from usmerge import equal_fre_merge
labels, edges = equal_fre_merge(data, n=3)

3. K-means Clustering:

from usmerge import kmeans_merge
labels, edges = kmeans_merge(data, n=3, max_iter=100)

Advanced Usage

1. 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)

2. Fuzzy C-Means:

from usmerge import fcm_merge
labels, edges = fcm_merge(data, n=3, m=2.0, max_iter=100, epsilon=1e-6)

3. Kernel Density Based:

from usmerge import kernel_density_merge
labels, edges = kernel_density_merge(data, n=3, bandwidth=None)

Return Values

All clustering methods return two values:

• labels: List of cluster labels for each data point
• edges: List of cluster boundaries

Example Analysis

import numpy as np
import matplotlib.pyplot as plt
from usmerge import som_k_merge, fcm_merge, kmeans_merge, hierarchical_density_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),
    'Hierarchical Density': hierarchical_density_merge(data, n=3)
}

# Visualize results
plt.figure(figsize=(15, 4))
for i, (name, (labels, edges)) in enumerate(methods.items(), 1):
    plt.subplot(1, 4, 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()

Parameters Guide

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)

Contributing

Feel free to contribute to this project by submitting issues or pull requests.

License

MIT License