non port: math/py-hdbscan/distinfo |
Number of commits found: 8 |
Wednesday, 16 Aug 2023
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18:25 Po-Chuan Hsieh (sunpoet)
math/py-hdbscan: Update to 0.8.33
- Convert to USE_PYTHON=pep517
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
4ab3cf5 |
Sunday, 9 Jul 2023
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21:34 Po-Chuan Hsieh (sunpoet)
math/py-hdbscan: Update to 0.8.30
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
b7d06b4 |
Friday, 30 Dec 2022
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09:05 Po-Chuan Hsieh (sunpoet)
math/py-hdbscan: Update to 0.8.29
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
232b048 |
Monday, 7 Mar 2022
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18:11 Po-Chuan Hsieh (sunpoet)
math/py-hdbscan: Update to 0.8.28
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
39276eb |
Wednesday, 17 Feb 2021
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18:08 sunpoet
Update to 0.8.27
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
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Tuesday, 31 Mar 2020
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23:03 sunpoet
Update to 0.8.26
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
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Saturday, 28 Mar 2020
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12:54 sunpoet
Update to 0.8.25
Changes: https://github.com/scikit-learn-contrib/hdbscan/releases
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Sunday, 29 Dec 2019
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12:46 sunpoet
Add py-hdbscan 0.8.24
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
Noise. Performs DBSCAN over varying epsilon values and integrates the result to
find a clustering that gives the best stability over epsilon. This allows
HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more
robust to parameter selection.
In practice this means that HDBSCAN returns a good clustering straight away with
little or no parameter tuning -- and the primary parameter, minimum cluster
size, is intuitive and easy to select.
HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm
that you can trust to return meaningful clusters (if there are any).
WWW: https://github.com/scikit-learn-contrib/hdbscan
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Number of commits found: 8 |