homemademachinelearning
hdbscan
Our great sponsors
homemademachinelearning  hdbscan  

7  6  
22,493  2,671  
  1.4%  
3.8  7.0  
9 months ago  3 months ago  
Jupyter Notebook  Jupyter Notebook  
MIT License  BSD 3clause "New" or "Revised" License 
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.
homemademachinelearning
 Homemade Machine Learning

✨ 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
hdbscan

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 sentencetransformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.
 Hierarchical clustering algorithm

Introduction to KMeans Clustering
Working in spatial data science, I rarely find applications where kmeans 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/scikitlearncontrib/hdbscan

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 gammalinkage variant which is quite powerful.

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 sklearncontrib so should be compatible with an sklearn pipeline.

[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/scikitlearncontrib/hdbscan/issues/69
What are some alternatives?
legomindstorms  My LEGO MINDSTORMS projects (using set 51515 electronics)
faiss  A library for efficient similarity search and clustering of dense vectors.
wordlesolver  For educational purposes, a simple script that assists in solving the word game Wordle.
Top2Vec  Top2Vec learns jointly embedded topic, document and word vectors.
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
Milvus  A cloudnative vector database, storage for next generation AI applications
rmi  A learned index structure
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:
PythonRobotics  Python sample codes for robotics algorithms.
RACplusplus  A high performance implementation of Reciprocal Agglomerative Clustering in C++
theelementsofstatisticallearning  My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
leidenalg  Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.