Analyzing the performance of different clustering algorithms with increasing dimensions

[sree0917]

Aim: Testing how the performance of different clustering algorithms for different datasets change on adding noise with different dimensions:

To be done: A jupyter notebook documentation describing the effect of the addition of different dimensions of noise on a dataset. Here different types of synthetic datasets are generated on which the experiment is performed. To these datasets gaussian noise of different dimensions are added, and the performance of each clustering algorithm is measured after noise addition. This is repeated for noise with different variances.

Expected output: The plots that compare the effect of varying noise dimensions on different clustering algorithms for each of the datasets. In this set of subplots, the variance of the added noise changes along the column and the dataset changes along the row.

Link to the code: https://nbviewer.jupyter.org/github/sree0917/team-forbidden-forest/blob/master/Sree/Clustering%20comparison%20%281%29.ipynb

[bdpedigo]

This issue is unclear about what you are actually proposing to PR into sklearn. You can be a lot more detailed about the fact that you are proposing a new tutorial, what the figures will be, what the data is