scikit-clarans
Scikit-learn compatible implementation of CLARANS and FastCLARANS for $k$-medoids clustering.
scikit-clarans implements $k$-medoids clustering in Python using the scikit-learn estimator interface. While $k$-means calculates artificial cluster centroids, $k$-medoids picks actual data points from the dataset as cluster centers.
Why use k-medoids?
- Outlier resistance: Medoids minimize absolute distances ($\sum d$) rather than squared Euclidean distances ($\sum d^2$), keeping centers stable when the dataset contains extreme values.
- Custom distance metrics: Supports metrics such as Manhattan, cosine, or precomputed distances, whereas standard $k$-means requires Euclidean space.
- Direct interpretability: Every cluster center corresponds to a real record in the input data.
Choosing between CLARANS and FastCLARANS
- FastCLARANS: Evaluates swaps across all $k$ medoids in a single pass using FastPAM1 delta calculations (Schubert & Rousseeuw, 2021). It uses $O(n)$ memory and is faster for most workloads.
- CLARANS: The classic randomized search algorithm from Ng & Han (2002). It supports delta cost evaluations with cached distances as well as brute-force recalculation.
Features
- Scikit-learn compatibility: Extends
BaseEstimatorandClusterMixinfor use inPipeline,GridSearchCV, and model evaluation workflows. - C acceleration: Core delta computations run in Cython with a pure Python and NumPy fallback.
- Distance routing: Automatically routes distance calculations to SciPy
cdistfor dense arrays, scikit-learnDistanceMetricfor sparse matrices, orpairwise_distances. - Low memory overhead: Computes distances as needed with $O(n)$ working memory instead of storing a full $O(n^2)$ matrix.
- Initialization options: Supports
k-medoids++,build,heuristic, uniform random sampling, or user-supplied medoid indices.
Installation
Install from PyPI:
pip install scikit-clarans
Install from source:
git clone https://github.com/ThienNguyen3001/scikit-clarans.git
cd scikit-clarans
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
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 evaluates swaps across all $k$ medoids simultaneously using the FastPAM1 formulation from Schubert & Rousseeuw (2021):
from clarans import FastCLARANS
fast_model = FastCLARANS(n_clusters=5, num_local=3, random_state=42)
fast_model.fit(X)
Differences from CLARANS:
- Samples non-medoid candidates instead of medoid-candidate pairs.
- Evaluates swaps against all $k$ medoids in a single pass ($O(k)$ fewer distance queries).
- Operates in $O(n)$ memory instead of $O(n^2)$.
Examples
Runnable scripts are located in the examples/ directory:
python examples/plot_quick_start.py
Documentation
Full API documentation and guides are available at https://scikit-clarans.readthedocs.io.
Contributing
Contributions and bug reports are welcome. See CONTRIBUTING.md for development setup and testing instructions.
Citation
If you use scikit-clarans in your research or software, 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 LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for scikit-clarans 0.3.3
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