Spectral Clustering Python Packages

spectralcluster

Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.

diarize

Speaker diarization for Python — "who spoke when?" CPU-only, no API keys, Apache 2.0. ~10.8% DER on VoxConverse, 8x faster than real-time.

betula-cluster

Memory-bounded clustering for large embeddings and tabular streams: BETULA cluster features in a Rust core, then k-means / GMM / Ward / spectral / Leiden / HDBSCAN on the compressed summary. Streaming partial_fit, scikit-learn API, PyO3, no LAPACK or SciPy at runtime.

communities

Library of graph clustering algorithms

sc-supertree

A supertree method for rooted source trees

corelay

CoRelAy is a tool to compose small-scale (single-machine) analysis pipelines.

autokluster

Auto-k spectral clustering for text embeddings

easyae-samuel-revolinski

This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data

topometry-nosc

An independently maintained, heavily modified fork of TopOMetry: geometry-first topological dimensionality reduction and manifold learning.

easyae

This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data