coreset
Implementation of lightweight coresets for data summarization (by OOub)
hammock-public
Visualize text embeddings (by colehaus)
| coreset | hammock-public | |
|---|---|---|
| 1 | 1 | |
| 2 | 30 | |
| - | - | |
| 10.0 | 2.3 | |
| almost 3 years ago | 10 months ago | |
| Python | HTML | |
| GNU General Public License v3.0 only | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
coreset
Posts with mentions or reviews of coreset.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-12.
hammock-public
Posts with mentions or reviews of hammock-public.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-12.
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K-Means Clustering
Funny, I did almost the exact same thing: https://github.com/colehaus/hammock-public. Though I project to 3D and then put them in an interactive 3D plot. The other fun little thing the interactive plotting enables is stepping through a variety of clustering granularities.
What are some alternatives?
When comparing coreset and hammock-public you can also consider the following projects:
generalized-kmeans-clustering - Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.