Time-series-classification-and-clustering-with-Reservoir-Computing VS hdbscan

Compare Time-series-classification-and-clustering-with-Reservoir-Computing vs hdbscan and see what are their differences.

Time-series-classification-and-clustering-with-Reservoir-Computing

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering. (by FilippoMB)
machine-learning-algorithms reservoir-computing time-series-classification time-series-clustering
Source Code
reservoir-computing.readthedocs.io
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hdbscan

A high performance implementation of HDBSCAN clustering. (by scikit-learn-contrib)
Machine Learning machine-learning-algorithms Clustering clustering-algorithm cluster-analysis clustering-evaluation
Source Code
hdbscan.readthedocs.io
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Time-series-classification-and-clustering-with-Reservoir-Computing hdbscan
1 6
320 2,672
- 0.6%
7.4 7.0
27 days ago 3 months ago
Python Jupyter Notebook
MIT License BSD 3-clause "New" or "Revised" License
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Time-series-classification-and-clustering-with-Reservoir-Computing

Posts with mentions or reviews of Time-series-classification-and-clustering-with-Reservoir-Computing. We have used some of these posts to build our list of alternatives and similar projects.

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