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# System prepended metadata

title: 'Meeting #3 (16/12/2021) Density Based Clustering'

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# Meeting #3 (16/12/2021) Density Based Clustering
11:00-12:00, Room 5001/Building 54


## Presentation

In this meeting, we briefly go through the following paper on clustering problem:

[**Density-based clustering via kernel diffusion.**](https://openreview.net/pdf?id=-geBFMKGlkq) 
Zheng, C., Chen. Y., Chen, C., Huang, J., and Hua, X.-S, *preprint*, 2021.

We also introduced the basic idea of the nonparametric kernel density estimation, density-based clustering, and some of their limitations with examples. Feel free to have a more detailed read on above preprint and  following relevant material if you are interested!


## Supplementary Reading

[A short tutorial of kernel density estimation (KDE)](http://faculty.washington.edu/yenchic/18W_425/Lec6_hist_KDE.pdf)
by Chen, Y.-C.  (University of Washington).


[Density-based spatial clustering of applications with noise (DBSCAN algorithm)](https://en.wikipedia.org/wiki/DBSCAN)
*Wikipedia*.

[Clustering by fast search and find of density peaks (DPC algorithm)](https://www.science.org/doi/10.1126/science.1242072)
by Rodriguez, A. and Laio, A., *Sciences*, 2014.


[Local contrast as an effective means to robust clustering against varying densities (LC-DPC algorithm)](https://link.springer.com/article/10.1007/s10994-017-5693-x)
by Chen, B., Ting, K., Washio, T., and Zhu, Y., *Machine Learning*, 2018.

