climb-astro 0.1.4

A versatile two-phase clustering algorithm designed for datasets with both known and exploratory components.

CLustering In Multiphase Boundaries (CLiMB)

A versatile two-phase clustering algorithm designed for datasets with both known and exploratory components.

Features

• Two-Phase Clustering: Combines constrained clustering with exploratory clustering to identify both known and novel patterns.
• Density-Aware: Uses local density estimation to intelligently filter and assign points.
• Flexible Exploratory Phase: Supports multiple clustering algorithms (DBSCAN, HDBSCAN, OPTICS) through a strategy pattern.
• Visualization Tools: Built-in 2D and 3D visualization capabilities for cluster analysis.
• Parameter Tuning: Builder pattern for flexible parameter adjustment.

Installation

pip install climb-astro

Or install from source:

git clone https://github.com/LorenzoMonti/CLiMB.git
cd CLiMB
pip install -e .

Quick Start

import numpy as np
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from CLiMB.core.CLiMB import CLiMB

# The number of centers to generate
centers = 4

# Generate synthetic data with 5 dimensions
X, y = make_blobs(n_samples=500, centers=centers, n_features=5, random_state=42)

# Scale the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Create seed points (optional)
seed_points = np.array([
    X[y == i].mean(axis=0) for i in range(centers)
])
seed_points_scaled = scaler.transform(seed_points)

# Initialize and fit CLiMB
climb = CLiMB(
    constrained_clusters=4,
    seed_points=seed_points_scaled,
    density_threshold=0.15,
    distance_threshold=2.5,
    radial_threshold=1.2,
    convergence_tolerance=0.05
)
climb.fit(X_scaled)

# Get cluster labels
labels = climb.get_labels()

# Visualize results (only possible in lower dimensions)
climb.inverse_transform(scaler)
fig = climb.plot_comprehensive_3d(save_path="./3d")
fig2 = climb.plot_comprehensive_2d(save_path="./2d")

Examples

See the examples/ directory for detailed usage examples:

• simple_example.py: Basic usage with well-defined clusters
• mixed_data_example.py: Handling mixed data with both convex and non-convex clusters
• compare_methods.py: Comparing different exploratory clustering methods

How It Works

CLiMB operates in two phases:

1. Constrained Phase (KBound): A modified K-means that:

   • Uses seed points to guide initial clustering
   • Applies density and distance constraints
   • Prevents centroids from drifting too far using radial thresholds

2. Exploratory Phase: Uses density-based clustering methods to discover patterns in points not assigned during the first phase.

Use Cases

CLiMB is particularly useful for:

• Datasets with partially known structure
• Astronomical data analysis
• Particle physics clustering
• Pattern discovery in scientific datasets
• Data exploration with prior knowledge

Advanced Usage

Using Different Exploratory Algorithms

from CLiMB.core.CLiMB import CLiMB
from CLiMB.exploratory.HDBSCANExploratory import HDBSCANExploratory

# Create HDBSCAN exploratory algorithm
hdbscan = HDBSCANExploratory(min_cluster_size=5, min_samples=3)

# Use it with CLIMB
climb = CLiMB(
    constrained_clusters=3,
    exploratory_algorithm=hdbscan
)

Parameter Tuning with Builder Pattern

climb = CLiMB()
climb.set_density(0.3) \
     .set_distance(2.5) \
     .set_radial(1.0) \
     .set_convergence(0.1)

License

MIT

Tests Status

Citation