Unsupervised Machine Learning Python Packages

pymcr

pyMCR: Multivariate Curve Resolution for Python

sequenzo

A fast, scalable, and intuitive Python package in sequence analysis.

anomaly-grid-py

Python bindings of a Rust library for anomaly detection in finite alphabets using variable-order Markov chains.

classixclustering

Fast and explainable clustering in Python

ascii-image-art

Python library for converting images into ASCII character strings and ASCII character colored images.

emmv

Metrics for unsupervised anomaly detection models

chunkey-bert

Modification of the KeyBERT method to extract keywords and keyphrases using chunks. This provides better results, especialy when handling long documents.

bhad

Python library for explainable Bayesian Anomaly Detection

stringlifier

Python module for detecting password, api keys hashes and any other string that resembles a randomly generated character sequence.

torchsom

torchsom: The Reference PyTorch Library for Self-Organizing Maps

pyroved

Invariant representation learning from imaging and spectral data

altanalyze3

AltAnalyze3: Advanced RNA-Seq Analysis Workflow

shapley-behaviors

Shapley value transformations for behavioral data analysis

louvainsplit

Efficient graph partitioning using LouvainSplit algorithm

stringlifier39

Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.

basata

BASATA - Code less while gaining more

ctec405library

Official Python Library for CTEC 405 – Applied Machine Learning & AI. Published by the Department of Electrical & Computer Engineering Technology – New York Institute of Technology.

simple-kmeans

Implementation of K-means algorithm from scratch with adaptive distance metrics

classipygrb

This repository contains all the updates, code, and documentation related to ClassiPyGRB.

spivae

Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.

segmentation-forests

Unsupervised segment discovery using divergence-based decision trees inspired by Random Forests

pyprotoclust

Python Prototype Clustering: an implementation of minimax linkage for hierarchical clustering

easyae-samuel-revolinski

easy way for non-linear dimensionality increase or reduction

easyae

easy way for non-linear dimensionality increase or reduction