minisom
DBCV
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| minisom | DBCV | |
|---|---|---|
| 3 | 1 | |
| 1,387 | 140 | |
| - | - | |
| 8.4 | 0.0 | |
| 5 days ago | 4 months ago | |
| Python | Python | |
| MIT License | MIT License |
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.
minisom
- How to use MiniSOM (Self Organizing Maps) Library
- [P][D] Self Organizing Maps
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[OC] Animation of a Self Organizing Map
I made this animation because I could not find a single decent demonstration of a SOM map on the internet, especially considering how popular it is becoming. I used the python library Pyvista for 3D plotting and creating the animation, and I used the minisom library for running the SOM.
DBCV
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Powerfull visualization tool : Dimensionality Reduction + Clustering + Unsupervised Score Metrics [P]
Estimate cluster quality using silhouette score or DBCV
What are some alternatives?
umap - Uniform Manifold Approximation and Projection
stringlifier - 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.
somoclu - Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters
DimReductionClustering
sparse-som - Efficient Self-Organizing Map for Sparse Data
awesome-community-detection - A curated list of community detection research papers with implementations.
susi - SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
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.