CarsonScott/HSOM
Hierarchical self-organizing maps for unsupervised pattern recognition
This tool helps researchers and data scientists discover hidden patterns and relationships within very complex, high-dimensional datasets without prior labeling. It takes in raw, unclassified data and outputs simplified, low-dimensional representations where similar patterns are grouped together, helping you make sense of large amounts of information. The primary users are data scientists or researchers dealing with rich datasets like image features, complex sensor readings, or genetic sequences.
No commits in the last 6 months.
Use this if you need to find inherent structures and cluster high-dimensional data into meaningful groups when you don't have predefined categories or labels.
Not ideal if you already have labeled data and want to train a model to classify new data into those existing categories.
Stars
61
Forks
11
Language
Python
License
—
Category
Last pushed
Dec 20, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/CarsonScott/HSOM"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
JustGlowing/minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
felixriese/susi
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
abhinavralhan/kohonen-maps
Implementation of SOM and GSOM
saeyslab/FlowSOM_Python
The complete FlowSOM package known from R, now available in Python!
LCSB-BioCore/GigaSOM.jl
Huge-scale, high-performance flow cytometry clustering in Julia