porterehunley/RACplusplus
A high performance implementation of Reciprocal Agglomerative Clustering in C++
This tool helps data scientists and machine learning engineers group large datasets into meaningful clusters. You input raw data points and it outputs cluster assignments for each data point, allowing you to categorize similar items without extensive manual sorting. It's designed for those working with millions of data entries who need to understand underlying structures.
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Use this if you need to perform hierarchical clustering on extremely large datasets, like hundreds of thousands or even millions of data points, and prioritize speed while maintaining cluster quality.
Not ideal if you need to visualize the full cluster hierarchy (dendrogram) or require linkage methods other than 'average', as these features are not yet fully implemented.
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Jupyter Notebook
License
MIT
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Last pushed
Sep 17, 2023
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