abojchevski/rsc
Robust Spectral Clustering. Implementation of "Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings".
When you have data points that naturally group together, like customer segments or different types of materials, but your measurements are messy or contain errors, this tool helps you find those hidden groups more accurately. You provide your noisy data, and it outputs clear assignments for each data point to its most probable group. Data scientists, researchers working with real-world measurements, or anyone analyzing complex datasets with potential data corruption would use this.
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Use this if your dataset is known to contain errors or 'corrupted' measurements, and you need to accurately identify underlying clusters.
Not ideal if your data is perfectly clean and free of noise, as standard clustering methods might suffice.
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26
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Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 23, 2019
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