Graph Clustering Algorithms (September 28, 2017)
ฝัง
- เผยแพร่เมื่อ 18 ก.ย. 2024
- Tselil Schramm (Simons Institute, UC Berkeley)
One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective.
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References from the Whiteboard:
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral
clustering: Analysis and an algorithm." Advances in neural information
processing systems. 2002.
Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway
spectral partitioning and higher-order cheeger inequalities." Journal
of the ACM (JACM) 61.6 (2014): 37.
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Additional Resources:
In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (www.cs.yale.edu..., focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings.
I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (www.simonsfoun....
License: CC BY-NC-SA 4.0
- creativecommon...
Awesome explanation!
Did you said MANSPLAINING (in 1:35) ???
LMAOOO she did
Quite a stupid thing to come out of the mouth of someone of her caliber!
@@russp622 Or, you know... It's a deliberate joke.
Hahaha she is hilarious!
lmaoo
I sometimes have the problem, that some of the clusters are not totally interconnected (one node is only accessible over nodes that are in other clusters ). How can I improve the algorithm, that this won't happen anymore? Is there any correlation between the number of clusters (K) and the number of eigenvectors i should use?
Great talk!
This helped me a lot
Thanks a lot
thank you