HDBSCAN, Fast Density Based Clustering, the How and the Why - John Healy
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- čas přidán 5. 07. 2024
- PyData NYC 2018
HDBSCAN is a popular hierarchical density based clustering algorithm with an efficient python implementation. In this talk we show how it works, why it works and why it should be among the first algorithms you use when exploring a new data set. Further we will show how we took an inherently O(n^2) algorithm and turned it into the O(nlogn) algorithm that is available in scikit-learn-contrib.
Slides - drive.google.com/file/d/1PgVu...
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Presentation Skills: 10/10
A very impressive presentation and algorithm! Thank you for teaching all this!
this is exactly what I have been looking for! great presentation.
Nice presentation, I see 200% confidence and eloquence
Wow I love the enthusiasm! It really makes it so much nicer to watch. Very insightful as well thank you very much!
Wow, what a great talk! Love the intuitive explanations and visuals. Super helpful. Thank you!
Absolutely fantastic presentation, thank you
Thank you so much. It was exactly what I was looking for 🎉🎉
Awesome presentation.
what an amazing speaker!
Sorry has to comment because of the kiiiiiiick ass animation! Brilliant.
that was a great talk!
great talk
15:30 there might be a misprint in the formula: d(X_i, X_j), not d(X_j, X_j)
Amazing
Thank you for the super interesting talk! I was wondering if you have worked with the new HDBSCAN integrated in sklearn 1.3.0? Is it possible to draw the cluster tree with this implementation?
Any luck?
Any idea why the GPU version of this method can't take a pre-computed distance matrix?
The coloring of the tree at 14:00 is needlessly confusing. See figure 3a in their paper McInnes & Healy 2017 to clarify things
27:50 Installation
can someone tell me about his linkedin or his full name please or how to connect to him
0:24 name and email
clustering is highly driven by the formatting of how the data relates to itself
and is near impossible to accomplish using a single method of approach.
Agree, but in practical terms, where do you start?
@@RoulDukeGonzo An intimate descriptive knowledge of the data is recommended.
Presentation Skills: 100000/10