minisom and kohonen-maps

These are competitors: MiniSom is a mature, widely-adopted SOM implementation suitable for production use, while Kohonen-maps is a lesser-known alternative that extends the basic SOM concept with GSOM (Growing Self-Organizing Maps) but lacks practical adoption.

minisom
73
Verified
kohonen-maps
57
Established
Maintenance 13/25
Adoption 10/25
Maturity 25/25
Community 25/25
Maintenance 10/25
Adoption 9/25
Maturity 16/25
Community 22/25
Stars: 1,576
Forks: 442
Downloads:
Commits (30d): 1
Language: Python
License: MIT
Stars: 73
Forks: 63
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No Dependents
No Package No Dependents

About minisom

JustGlowing/minisom

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

This tool helps researchers and students understand complex, high-dimensional data by converting it into simpler, visual relationships on a 2D map. You input your raw data, like a table of observations, and it outputs a 'map' where similar data points are clustered together. Data analysts, scientists, or anyone dealing with complex datasets can use this to identify patterns or outliers.

data-analysis pattern-recognition data-visualization unsupervised-learning anomaly-detection

About kohonen-maps

abhinavralhan/kohonen-maps

Implementation of SOM and GSOM

This tool helps data analysts and researchers understand complex datasets by visually organizing high-dimensional data into intuitive, low-dimensional maps. You input raw data, and it outputs visual maps like U-Matrices or hit maps, showing how data points cluster and relate. It's ideal for anyone needing to identify patterns, segment groups, or spot anomalies within their data.

customer-segmentation market-research bioinformatics document-analysis data-exploration

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