hdbscan
groupImg
| hdbscan | groupImg | |
|---|---|---|
| 6 | 2 | |
| 2,675 | 222 | |
| 0.7% | - | |
| 5.8 | 4.9 | |
| 5 days ago | 1 day ago | |
| Jupyter Notebook | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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hdbscan
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Introducing the Semantic Graph
A number of excellent topic modeling libraries exist in Python today. BERTopic and Top2Vec are two of the most popular. Both use sentence-transformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.
- Hierarchical clustering algorithm
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Introduction to K-Means Clustering
Working in spatial data science, I rarely find applications where k-means is the best tool. The problem is that it is difficult to know how many clusters you can expect on maps. Is it 5, 500, or 10,000? Here HDBSCAN [1] shines because it will cluster _and_ select the most suitable number of clusters, to cut the single linkage cluster tree.
[1]: https://github.com/scikit-learn-contrib/hdbscan
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New clustering algorithms like DBSCAN and OPTICS?
You might be interested in HDBSCAN which has several implementations, but the python implelementation is commonly used. That implementation makes use of algorithmic changes to significantly improve the computational complexity. Some more recent variations on that include the gamma-linkage variant which is quite powerful.
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DBSCAN ALternatives?
The OPTICS algorithm is in the latest versions of sklearn and is a reasonable alternative to DBSCAN -- it has much the same theoretical foundation, but can cope with variable density clusters better. If you are willing to step outside sklearn itself there is also HDBSCAN which is a hierarchical clustering version of DBSCAN and is in sklearn-contrib so should be compatible with an sklearn pipeline.
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[D] Good algorithm for clustering big data (sentences represented as embeddings)?
Maybe use (H)DBScan which I think should work also for huge datasets. I don't think there is a ready to use clustering with unbuild cosine similarily metrics, and you also won't be able to precompute the 100k X 100k dense similarity matrix. The only way to go on this is to L2 normalize your embeddings, then the dot product will be the angular distance as a proxy to the cosine similarily. See also https://github.com/scikit-learn-contrib/hdbscan/issues/69
groupImg
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Introduction to K-Means Clustering
If anyone is interested, I have two projects that uses k-means
https://github.com/victorqribeiro/groupImg
https://github.com/victorqribeiro/budget
Being one of the first ML algorithms that I learned, I spend some time finding use cases for it
If I'm not mistaken I've also used in to classify deforestation in an exercise
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Show HN: I made NIGHT.FM, a cyberpunk-inspired online radio
well, I took a class back in college with an Old School professor. He's the one who drove the class that way. I just enjoyed the process. But there are a lot of tutorials on the internet about writing your own NN. I think the first algorithm that I ever wrote regarding ML was k-means [1]. Start there and see where it takes you:
https://en.wikipedia.org/wiki/K-means_clustering
Look at this project I have used it:
https://github.com/victorqribeiro/groupImg
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
Milvus - A cloud-native vector database, storage for next generation AI applications
RobotEyes - Image comparison for Robot Framework
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
tslearn - The machine learning toolkit for time series analysis in Python
RACplusplus - A high performance implementation of Reciprocal Agglomerative Clustering in C++
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
img2cmap - Create colormaps from images