5uso/HiPGMC
HiPGMC is a highly efficient, parallel Graph-based Multi-view Clustering implementation.
This is a powerful tool for data scientists and researchers who need to group data points that have multiple different descriptions or 'views' into meaningful clusters. It takes in datasets where each item has several sets of features (like an image described by color, texture, and shape) and outputs assignments of each item to specific clusters. This is ideal for quickly finding patterns in complex, multi-faceted data.
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Use this if you need to perform clustering on large datasets with multiple feature sets and require highly efficient, parallel processing to handle the computational load.
Not ideal if you are working with simple, single-view datasets or do not have access to a high-performance computing environment with MPI and OpenMP.
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MIT
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Last pushed
Feb 29, 2024
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