Score-SG
Use algorithm="score-sg" to select the approximate anti-hub reinforced
graph-construction path.
Score-SG is the scalable path to use when the traditional exact
algorithm="core-sg" construction becomes limited by n_samples. The
exact path builds dense pairwise distance information; Score-SG avoids that
dense all-pairs stage by using an approximate sparse-neighbor graph.
from core_sg import CoreSGClusterer
clusterer = CoreSGClusterer(
k_max=15,
algorithm="score-sg",
metric="euclidean",
random_state=42,
approx_knn_kwargs={"n_trees": 8},
)
clusterer.fit(X, k=10)
Score-SG uses PyNNDescent to build an approximate nearest-neighbor graph, derives approximate core-distance lists, selects anti-hubs by directed in-degree, adds support edges among selected anti-hubs, and then reuses the same MST and hierarchy machinery as the classical path.
In the current benchmark pages, Score-SG is also the best-performing method in
the common repeated multi-k comparison against exact CoreSG and HDBSCAN.
See ScoreSG Performance Results for the measured speedups and
extended scaling results.
Important Behaviors
random_state controls random tie-breaking during anti-hub selection.
approx_knn_kwargs is forwarded to PyNNDescent.
anti_hubs_ is available only when algorithm="score-sg".
Score-SG may fail explicitly if the constructed support graph is disconnected, because MST extraction requires a connected support graph.