homemade-machine-learning VS hdbscan

Compare homemade-machine-learning vs hdbscan and see what are their differences.


🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained (by trekhleb)
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homemade-machine-learning 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 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.


Posts with mentions or reviews of homemade-machine-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-14.


Posts with mentions or reviews of hdbscan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-16.
  • Introducing the Semantic Graph
    5 projects | dev.to | 16 Sep 2022
    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
    1 project | /r/learnmachinelearning | 15 Apr 2022
  • Introduction to K-Means Clustering
    5 projects | news.ycombinator.com | 14 Mar 2022
    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

  • New clustering algorithms like DBSCAN and OPTICS?
    1 project | /r/MLQuestions | 11 Jan 2022
    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.
  • DBSCAN ALternatives?
    1 project | /r/MLQuestions | 26 Dec 2021
    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.
  • [D] Good algorithm for clustering big data (sentences represented as embeddings)?
    5 projects | /r/MachineLearning | 31 Mar 2021
    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

What are some alternatives?

When comparing homemade-machine-learning and hdbscan you can also consider the following projects:

lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)

faiss - A library for efficient similarity search and clustering of dense vectors.

wordle-solver - 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 cloud-native 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++

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

leidenalg - Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.