ckwrap VS hdbscan

Compare ckwrap vs hdbscan and see what are their differences.

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ckwrap hdbscan
1 6
12 2,726
- 1.0%
6.9 6.9
3 months ago 10 days ago
Python Jupyter Notebook
GNU Lesser General Public License v3.0 only 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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ckwrap

Posts with mentions or reviews of ckwrap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-14.
  • Introduction to K-Means Clustering
    5 projects | news.ycombinator.com | 14 Mar 2022
    Note also that specifically for one-dimensional data, there is a globally optimal solution to the k-means clustering problem. There is an R package that implements it using a C++ core implementation [1], and also a Python wrapper [2].

    [1]: https://cran.r-project.org/package=Ckmeans.1d.dp

    [2]: https://github.com/djdt/ckwrap

hdbscan

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 ckwrap and hdbscan you can also consider the following projects:

word2vec - Automatically exported from code.google.com/p/word2vec

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

Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.

Milvus - A cloud-native vector database, storage for next generation AI applications

RACplusplus - A high performance implementation of Reciprocal Agglomerative Clustering in C++

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:

homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

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

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

sentence-transformers - Multilingual Sentence & Image Embeddings with BERT

umap - Uniform Manifold Approximation and Projection

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