BGU-CS-VIL/pdc-dp-means
"Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]
This tool helps data scientists and machine learning engineers quickly group unlabeled data points into distinct categories when they don't know how many groups exist beforehand. You provide your raw data, and it identifies and assigns each data point to its most appropriate cluster. It's especially useful for handling large datasets or real-time streams.
No commits in the last 6 months. Available on PyPI.
Use this if you need to perform fast, scalable cluster analysis on large datasets where the number of clusters is unknown, or if you need an online clustering solution for real-time data.
Not ideal if you already know the exact number of clusters you need and prefer algorithms designed specifically for that scenario.
Stars
22
Forks
4
Language
Python
License
BSD-3-Clause
Category
Last pushed
Jul 20, 2024
Commits (30d)
0
Dependencies
2
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