hdbscan
homemade-machine-learning
| hdbscan | homemade-machine-learning | |
|---|---|---|
| 6 | 7 | |
| 2,675 | 22,545 | |
| 0.7% | - | |
| 5.8 | 3.8 | |
| 3 days ago | 10 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| 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
homemade-machine-learning
- Homemade Machine Learning
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✨ 5 Best GitHub Repositories to Learn Machine Learning in 2022 for Free 💯
4️⃣ Homemade Machine Learning
- Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
- Homemade Machine Learning: Python examples of popular machine learning algorithms
- Homemade Machine Learning: Python examples of popular machine learning algorithms with interactive Jupyter demos
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
wordle-solver - For educational purposes, a simple script that assists in solving the word game Wordle.
Milvus - A cloud-native vector database, storage for next generation AI applications
PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features
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:
rmi - A learned index structure
RACplusplus - A high performance implementation of Reciprocal Agglomerative Clustering in C++
PythonRobotics - Python sample codes for robotics algorithms.
leidenalg - Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman