clustor 0.1.0
Rust-accelerated high-performance clustering algorithms.
Requires Python: >=3.10
Keywords: clustering, kmeans, dbscan, optics, gmm, birch, hierarchical-clustering, machine-learning

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Clustor

</h1>

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Clustor is a Rust-accelerated clustering toolkit with a Python-first API built on PyO3 and
maturin. It delivers fast, minimal-dependency implementations of common clustering algorithms and
cluster validation metrics for use inside production data pipelines.

Clustor focuses on unsupervised learning primitives. Use it alongside dataset balancing tools when
you need reweighting or resampling.

> [!CAUTION]
> Active development. APIs may evolve before a 1.0 release.

## Features

- KMeans and MiniBatchKMeans with KMeans++ initialization, `n_init`, Euclidean/Cosine metrics,
  optional normalization, `sample_weight`, and streaming `partial_fit`.
- BisectingKMeans for hierarchical top-down splitting.
- DBSCAN with noise labeling and core sample indices.
- OPTICS reachability ordering and core distances.
- Affinity Propagation for exemplar-based clustering.
- BIRCH streaming clustering using CF-tree summaries.
- GaussianMixture (EM, diagonal covariance): `predict_proba`, `score_samples`, `score`, `aic`,
  `bic`, `sample`, and model selection metrics.
- Cluster validation: Silhouette, Calinski–Harabasz, Davies–Bouldin.
- HAC linkage (dendrogram output) with SciPy-style linkage matrices.

## Installation

### Python (recommended)

Use a virtual environment, then install build tools and the package:

```bash
python -m venv .venv
. .venv/bin/activate
pip install -U pip
pip install -U maturin numpy
maturin develop --release
```

### Local development

```bash
python -m venv .venv
. .venv/bin/activate
pip install -U pip
pip install -U maturin numpy pytest pre-commit
pre-commit run --all-files
maturin develop --release
pytest -q
```

## Quick usage

```python
import numpy as np
import clustor

X = np.vstack([np.random.randn(200, 2), np.random.randn(200, 2) + 6])

km = clustor.KMeans(n_clusters=2, n_init=10, random_state=0)
result = km.fit(X)
print(result["inertia"])

op = clustor.OPTICS(min_samples=10, max_eps=1.0)
res = op.fit(X)
print(res["reachability"].shape, res["ordering"].shape)

ap = clustor.AffinityPropagation(damping=0.7)
res = ap.fit(X)
print("clusters:", len(res["exemplars"]), "converged:", res["converged"])

br = clustor.Birch(threshold=0.7, branching_factor=30, n_clusters=2)
res = br.fit(X)
print(res["centers"].shape)

Z = clustor.hac_dendrogram(X, method="average", metric="euclidean")
print(Z.shape)  # (n-1, 4)
```

## Advanced usage

### Streaming MiniBatchKMeans

```python
mb = clustor.MiniBatchKMeans(n_clusters=2, batch_size=2, max_steps=20, random_state=0)
mb.fit(X[:3])
mb.partial_fit(X[3:])
print(mb.predict(X))
```

Output:

```
[0 0 0 1 1 1]
```

### GaussianMixture model selection metrics

```python
gm = clustor.GaussianMixture(2, max_iter=50, tol=1e-4, random_state=0)
gm.fit(X)
print(gm.score_samples(X))
print(gm.score(X))
print(gm.aic(X))
print(gm.bic(X))
```

Output:

```
[-1.526949 -2.276946 -2.276946 -1.526949 -2.276946 -2.276946]
-2.026946850203142
42.3233622024377
40.4491974254902
```

## Examples with outputs

The following examples use a fixed dataset and deterministic seeds to keep outputs stable.

```python
import numpy as np
import clustor

np.set_printoptions(precision=6, suppress=True)
X = np.array(
    [
        [0.0, 0.0],
        [0.0, 1.0],
        [1.0, 0.0],
        [10.0, 10.0],
        [10.0, 11.0],
        [11.0, 10.0],
    ],
    dtype=np.float64,
)
```

### KMeans

```python
km = clustor.KMeans(n_clusters=2, n_init=1, max_iter=50, random_state=0)
km_out = km.fit(X)
print(km_out["centers"])
print(km_out["labels"])
```

Output:

```
[[10.333333 10.333333]
 [ 0.333333  0.333333]]
[1 1 1 0 0 0]
```

### MiniBatchKMeans

```python
mb = clustor.MiniBatchKMeans(n_clusters=2, batch_size=3, max_steps=200, random_state=0)
mb_out = mb.fit(X)
print(mb_out["centers"])
print(mb.predict(X))
```

Output:

```
[[10.374126 10.300699]
 [ 0.33121   0.321656]]
[1 1 1 0 0 0]
```

### BisectingKMeans

```python
bk = clustor.BisectingKMeans(n_clusters=2, n_init=1, max_iter=50, random_state=0)
bk_out = bk.fit(X)
print(bk_out["centers"])
print(bk.predict(X))
```

Output:

```
[[10.333333 10.333333]
 [ 0.333333  0.333333]]
[1 1 1 0 0 0]
```

### DBSCAN

```python
db = clustor.DBSCAN(eps=1.6, min_samples=2)
db_out = db.fit(X)
print(db_out["labels"])
print(db_out["core_sample_indices"])
```

Output:

```
[0 0 0 1 1 1]
[0 1 2 3 4 5]
```

### OPTICS

```python
opt = clustor.OPTICS(min_samples=2, max_eps=2.0)
opt_out = opt.fit(X)
print(opt_out["ordering"])
print(opt_out["reachability"])
print(opt_out["core_distances"])
print(opt_out["predecessor"])
```

Output:

```
[0 2 1 3 5 4]
[inf  1.  1. inf  1.  1.]
[1. 1. 1. 1. 1. 1.]
[-1  0  0 -1  3  3]
```

### Affinity Propagation

```python
ap = clustor.AffinityPropagation(damping=0.6, max_iter=200, convergence_iter=15)
ap_out = ap.fit(X)
print(ap_out["exemplars"])
print(ap_out["labels"])
print(ap_out["converged"])
```

Output:

```
[0 3]
[0 0 0 1 1 1]
True
```

### Birch

```python
br = clustor.Birch(threshold=1.5, branching_factor=10, n_clusters=2)
br_out = br.fit(X)
print(br_out["centers"])
print(br_out["labels"])
print(br_out["n_subclusters"])
```

Output:

```
[[10.333333 10.333333]
 [ 0.333333  0.333333]]
[1 1 1 0 0 0]
2
```

### GaussianMixture

```python
gm = clustor.GaussianMixture(2, max_iter=50, tol=1e-4, random_state=0)
gm_out = gm.fit(X)
print(gm_out["weights"])
print(gm_out["means"])
print(gm_out["covars"])
print(gm_out["converged"])
```

Output:

```
[0.5 0.5]
[[10.333333 10.333333]
 [ 0.333333  0.333333]]
[[0.222223 0.222223]
 [0.222223 0.222223]]
True
```

### Validation metrics

```python
labels = km_out["labels"]
print(clustor.silhouette_score(X, labels))
print(clustor.calinski_harabasz_score(X, labels))
print(clustor.davies_bouldin_score(X, labels))
```

Output:

```
0.9196222281154851
450.00000000000017
0.09249505911485287
```

### HAC linkage

```python
Z = clustor.hac_dendrogram(X, method="average", metric="euclidean")
print(Z)
```

Output:

```
[[ 0.        1.        1.        2.      ]
 [ 3.        4.        1.        2.      ]
 [ 6.        2.        1.207107  3.      ]
 [ 7.        5.        1.207107  3.      ]
 [ 8.        9.       14.165681  6.      ]]
```

## API overview

### Estimators

- `KMeans`, `MiniBatchKMeans`, `BisectingKMeans`
- `DBSCAN`
- `OPTICS`
- `AffinityPropagation`
- `Birch`
- `GaussianMixture`

All estimators accept NumPy arrays and return NumPy arrays for outputs. Methods that accept
`sample_weight` validate length consistency with the input data.

### Functional APIs

- `kmeans`, `bisecting_kmeans`, `dbscan`, `gaussian_mixture`
- `optics`, `affinity_propagation`, `birch`, `hac_dendrogram`

### Metrics

- `silhouette_score`, `calinski_harabasz_score`, `davies_bouldin_score`

## Data handling and performance

- Inputs are converted to contiguous `float64` NumPy arrays for predictable performance.
- Heavy computation runs outside the Python GIL for improved multi-threaded throughput when
  Python threads are present.
- Algorithms are implemented in Rust with minimal dependencies to keep builds lightweight.

## Example notebooks

Each notebook is executed with deterministic inputs and includes narrative context plus outputs.

- `examples/01_kmeans_family.ipynb`: KMeans, MiniBatchKMeans, and BisectingKMeans.
- `examples/02_density_clustering.ipynb`: DBSCAN and OPTICS.
- `examples/03_affinity_birch.ipynb`: Affinity Propagation and Birch.
- `examples/04_gaussian_mixture_metrics.ipynb`: GaussianMixture and validation metrics.
- `examples/05_hac_linkage.ipynb`: HAC linkage matrices.

## Production readiness

- Deterministic results when `random_state` is provided, including in the notebooks.
- Strict input validation for shape mismatches, empty datasets, and invalid cluster counts.
- GIL-free Rust kernels for improved parallel throughput in Python pipelines.
- Typed Python API surface (`py.typed`) with docstrings that include args/returns/examples.

## Edge cases and validation

- Estimators raise `ValueError` for empty inputs, invalid shapes, or non-positive `n_clusters`.
- Weight-aware estimators validate that `sample_weight` has the same length as `X`.
- `MiniBatchKMeans.partial_fit` requires a prior `fit` call and consistent feature dimensions.
- Validation metrics ignore noise labels (`-1`) and require at least two non-noise clusters; they
  raise `ValueError` when the input data or label configuration is invalid.

## Testing and quality

- Rust tests: `cargo test`
- Python tests: `pytest`
- Linting and formatting: `pre-commit run --all-files`

## Citation
If you use clustor in your work and wish to refer to it, please use the following BibTeX entry.
```bibtex
@software{clustor,
  author = {Soumyadip Sarkar},
  title = {Clustor: Rust-accelerated High-Performance Clustering Algorithms},
  year = {2026},
  url = {https://github.com/alphavelocity/clustor}
}
```

## License
This project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.