Mixclu VS ML-Workspace

The package is still in progress, and I plan to add deep learning-based methods such as autoencoder and other techniques. I also plan to implement a few papers based on mixed clustering problem statements.Package link: https://github.com/monk1337/Mixclu

Today I am releasing the first version of mixclu package for mix data clustering. Please check it out and give it a star if you like it. Mixclu (Mix Clustering ) is an open-source python package for doing unsupervised mix data types clustering. This package puts together a variety of combination models, including kmeans-one-hot, Gower distance, umap, etc. The goal is to provide an easy-to-use implementation for each algorithm.

Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.

The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)

It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)

I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.