awesome-community-detection VS minisom

Compare awesome-community-detection vs minisom and see what are their differences.

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awesome-community-detection minisom
1 3
2,266 1,387
- -
2.8 8.4
about 1 month ago 6 days ago
Python Python
Creative Commons Zero v1.0 Universal MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

awesome-community-detection

Posts with mentions or reviews of awesome-community-detection. We have used some of these posts to build our list of alternatives and similar projects.

minisom

Posts with mentions or reviews of minisom. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-15.

What are some alternatives?

When comparing awesome-community-detection and minisom you can also consider the following projects:

Ringer-Client - Ringer is a new messaging app for windows. Its currently in beta but lets work together to make it better. The code here is snapshot code so it might not work as intended. To get the latest stable build pls download the installer.

umap - Uniform Manifold Approximation and Projection

DBCV - Python implementation of Density-Based Clustering Validation

somoclu - Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters

Clover - An Efficient DNA Clustering algorithm based on Tree Structure.

sparse-som - Efficient Self-Organizing Map for Sparse Data

susi - SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

som-tsp - Solving the Traveling Salesman Problem using Self-Organizing Maps

n2d - A deep clustering algorithm. Code to reproduce results for our paper N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding.