  Density-based clustering is a commonly used tool in data science. Today many
data science works are utilizing high-dimensional neural embeddings. However,
traditional density-based clustering techniques like DBSCAN have a degraded
performance on high-dimensional data. In this paper, we propose LAF, a generic
learned accelerator framework to speed up the original DBSCAN and the
sampling-based variants of DBSCAN on high-dimensional data with angular
distance metric. This framework consists of a learned cardinality estimator and
a post-processing module. The cardinality estimator can fast predict whether a
data point is core or not to skip unnecessary range queries, while the
post-processing module detects the false negative predictions and merges the
falsely separated clusters. The evaluation shows our LAF-enhanced DBSCAN method
outperforms the state-of-the-art efficient DBSCAN variants on both efficiency
and quality.
