hdbscan
Top2Vec
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hdbscan | Top2Vec | |
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6 | 13 | |
2,672 | 2,839 | |
1.4% | - | |
7.0 | 7.0 | |
3 months ago | 5 months ago | |
Jupyter Notebook | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" 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
Top2Vec
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[D] Is it better to create a different set of Doc2Vec embeddings for each group in my dataset, rather than generating embeddings for the entire dataset?
I'm using Top2Vec with Doc2Vec embeddings to find topics in a dataset of ~4000 social media posts. This dataset has three groups:
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Tips for best Top2Vec (HDBSCAN) usage
I asked in a previous post for advice about how to find insight in unstructured text data. Almost everyone recommended BERTopic, but I wasn't able to run BERTopic on my machine locally (segmentation fault). Fortunately, I found Top2Vec, which uses HBDSCAN and UMAP to quickly find good topics in uncleaned(!) text data.
- How can I group domain specific keywords based on their word embeddings?
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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.
- Top2Vec: Embed topics, documents and word vectors
- How to cluster articles about software vulnerabilities?
- Ciencia de Dados - Classificacao de texto
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Extracting topics from 250k facebook posts
Since you already have the facebook posts, you can use top2vec https://github.com/ddangelov/Top2Vec
- [D] Good algorithm for clustering big data (sentences represented as embeddings)?
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SOTA for Topic Modeling
Here's an implementation that uses UMAP and HDBSCAN: https://github.com/ddangelov/Top2Vec but you could use a semi-supervised algorithm in the clustering step if you wanted specific topics.
What are some alternatives?
faiss - A library for efficient similarity search and clustering of dense vectors.
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Milvus - A cloud-native vector database, storage for next generation AI applications
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
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:
RACplusplus - A high performance implementation of Reciprocal Agglomerative Clustering in C++
homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
GuidedLDA - semi supervised guided topic model with custom guidedLDA
leidenalg - Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.