2 votes

What's the fastest clustering package in Python?

Should be fairly easy to asses the computational requirements - just try it out without worrying about the accuracy of the model. I don't know if the packages comes with the specific clustering ...
nammerkage's user avatar
2 votes
Accepted

What's the fastest clustering package in Python?

Depending on your platform, processor, memory, etc, you may want to check out https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html Some of the clustering algorithms are ...
brewmaster321's user avatar
2 votes

How to use spectral clustering to predict?

If the paper didn't elaborate on this, it must mean that they do 1). They look for the 10 closest neighbors of a new point and use majority voting to assign a cluster. On a side note, strange that ...
Valentin Calomme's user avatar
1 vote

What's the fastest clustering package in Python?

I would go with HDBSCAN, a hierarchical version of the DBSCAN algo. It is not necessarily easy to install so might want to go with the sklearn DBSCAN implementation.
Lucas Morin's user avatar
  • 2,093
1 vote

What are practical differences between kernel k-means and spectral clustering?

The differences are indeed not too large. There is a paper called Kernel k-means, Spectral Clustering and Normalized Cuts by Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis from KDD 2004 that is ...
Make42's user avatar
  • 752
1 vote

MEL VS linear spectrograms for bioacoustics machine learning

The most important is to set the parameters of the mel spectrogram appropriately. This means both the temporal resolution, the frequency range and frequency resolution. For example, with ...
Jon Nordby's user avatar
  • 1,482
1 vote
Accepted

What are the benefits of using spectral k-means over simple k-means?

They are totally different approaches. Spectral Embedding is a representation of your data and it maps close data points next to each other in a new feature space. This helps k-means to deal with more ...
Kasra Manshaei's user avatar
1 vote
Accepted

Clusterize Spectrum

This sounds like a normal supervised classification task. Have you tried other standard methods like Support Vector Machines, RandomForests, Gradient Boosting, kNN, Neural Networks etc. as well or is ...
Tinu's user avatar
  • 508

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