deltric 0.3.1

Delaunay Triangulation Clustering: density-robust clustering with built-in outlier detection

DelTriC - Delaunay Triangulation Clustering

Density-robust clustering with built-in outlier detection.

DelTriC is a clustering algorithm based on Delaunay triangulation that excels at simultaneously finding clusters of arbitrary shape and detecting outliers.

Key Features

• No need to specify k - automatically determines the number of clusters
• Arbitrary cluster shapes - not limited to spherical clusters
• Built-in outlier detection - identifies anomalies as a first-class output
• Auto-param mode - sensible defaults computed from your data
• scikit-learn compatible - fit(), fit_predict(), get_params(), set_params()

Installation

pip install deltric

Quick Start

from deltric import DelTriC

model = DelTriC()
labels = model.fit_predict(X)

# labels == -1  => outlier
# labels >=  0  => cluster assignment

How It Works

1. Project high-dimensional data to 2D using UMAP or PCA
2. Triangulate via Delaunay - every point connected to its natural neighbors
3. Prune edges longer than a statistically-thresholded length
4. Extract clusters as connected components of the remaining graph
5. Detect outliers - small connected components and isolated points

API

DelTriC(
    prune_param="auto",
    merge_param="auto",
    min_cluster_size=10,
    dim_reduction="auto",
    back_proj=True,
    anomaly_sensitivity="auto",
    prune_mode="neighbor_bridge",
    prune_regime="auto",
    projected_hard_limit=True,
    random_state=42,
)