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scikit-clarans

A scikit-learn compatible implementation of CLARANS and FastCLARANS for scalable $k$-medoids clustering.

License DOI Python 3.9+ Docs Build Test Suite Quality Check PyPI version Open In Colab

scikit-clarans brings scalable $k$-medoids clustering to Python with a native scikit-learn API. Unlike $k$-means which computes artificial centroids (means), $k$-medoids picks actual data points as cluster centers.

Why k-Medoids over k-Means?

  • Outlier Robust: Minimizes absolute distance ($\sum d$) rather than squared Euclidean distance ($\sum d^2$), so extreme values won't skew cluster centers.
  • Custom Distance Metrics: Works with cosine, manhattan, euclidean, or any valid metric—unlike $k$-means which is strictly Euclidean.
  • Directly Interpretable: Medoids are real observations from your dataset (e.g., representative user profiles, real molecules, exemplary documents).

CLARANS vs. FastCLARANS: Which one to use?

  • FastCLARANS (Recommended for most workloads): Uses FastPAM1 delta calculations (Schubert & Rousseeuw, 2021) to evaluate all $k$ medoids at once. Explores $k$ graph edges in the time CLARANS explores one, yielding substantial speedups with $O(n)$ memory and native C-extension acceleration.
  • CLARANS: Randomized search (Ng & Han, 2002) with optional distance caching (cost_evaluation='delta', default) for fast $O(n)$ swap evaluations, or classic brute-force cost recalculation (cost_evaluation='brute_force').

Features

  • Scikit-Learn Native: Inherits from BaseEstimator and ClusterMixin. Plug-and-play in scikit-learn Pipeline, GridSearchCV, and clustering evaluations.
  • Cython & C-Accelerated: Core delta cost updates and cache tracking are accelerated with compiled C-extensions (Cython), with seamless fallback to pure Python/NumPy if C extensions are unavailable.
  • Cascading Distance Engine: Automatically routes distance computations through the fastest available engine: SciPy cdist (C-kernel for dense arrays), Scikit-Learn DistanceMetric (for sparse CSR matrices & callables), or pairwise_distances.
  • Memory Efficient: Computes distances on-the-fly ($O(n)$ memory overhead) to easily scale to tens of thousands of samples without blowing up RAM ($O(n^2)$).
  • Flexible Seeding: Supports multiple initialization strategies (k-medoids++, build, heuristic, random, or custom array).

Installation

Install simply via pip:

pip install scikit-clarans

Or install from source:

pip install .

For development

pip install -e ".[dev]"

Quick Start

CLARANS

from clarans import CLARANS
from sklearn.datasets import make_blobs

# 1. Create dummy data
X, _ = make_blobs(n_samples=1000, centers=5, random_state=42)

# 2. Initialize CLARANS
#    - n_clusters: 5 clusters
#    - num_local: 3 restarts for better quality
#    - init: 'k-medoids++' for smart starting points
#    - cost_evaluation: 'delta' (default) for fast O(n) swap evaluations; 'brute_force' for classic baseline
clarans = CLARANS(n_clusters=5, num_local=3, init='k-medoids++', cost_evaluation='delta', random_state=42)

# 3. Fit
clarans.fit(X)

# 4. Results
print("Medoid Indices:", clarans.medoid_indices_)
print("Labels:", clarans.labels_)

FastCLARANS

FastCLARANS implements the faster variant from Schubert & Rousseeuw (2021). It evaluates swaps with all k medoids simultaneously using FastPAM1 delta formulas, exploring k edges of the search graph in the time CLARANS explores one:

from clarans import FastCLARANS

# FastCLARANS computes distances on-the-fly (memory efficient)
# and samples max(250, 2.5% of non-medoid points) per iteration
fast_model = FastCLARANS(n_clusters=5, num_local=3, random_state=42)
fast_model.fit(X)

Key differences from CLARANS:

  • Samples only non-medoid candidates (not medoid-candidate pairs)
  • Evaluates swap with all k medoids at once (O(k) speedup per evaluation)
  • Memory efficient: O(n) instead of O(n²)

Examples

This repository includes a number of runnable examples in the examples/ folder showing common usage patterns and integrations. Run any example with:

python examples/01_quick_start.py

Documentation

For full API reference and usage guides, please see the Documentation.

Contributing

Contributions are welcome! Please check out CONTRIBUTING.md for guidelines.

Citation

If you use scikit-clarans in your software or research, please cite:

@software{scikit_clarans,
  author       = {Nguyen, Ngoc Thien},
  title        = {scikit-clarans: A Python Library for CLARANS Clustering},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18366801},
  url          = {https://github.com/ThienNguyen3001/scikit-clarans}
}

Academic References

The core algorithms implemented in this package originate from:

  • CLARANS:

    Ng, R. T., & Han, J. (2002). CLARANS: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1003-1016. doi:10.1109/TKDE.2002.1033770

  • FastCLARANS & FastPAM1:

    Schubert, E., & Rousseeuw, P. J. (2021). Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Information Systems, 101, 101804. doi:10.1016/j.is.2021.101804

  • Seeding & Initialization:

    Initialization strategies (k-medoids++, heuristic, build) are adapted from the scikit-learn-extra KMedoids implementation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Metadata

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Release history Release notifications | RSS feed

0.3.4

26 release files

0.3.2

26 release files

0.3.1

26 release files

This release

0.3.0 This release

26 release files

0.2.2

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0.2.1

2 release files

0.2.0

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

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