CODEWITHSUNDEEP
cluster-analysisdata-science
Shreya Pandit
Shreya Pandit

Reputation: 11

Visualizing clusters using TSNE

I have a dataset which I need to cluster and display in a way wherein elements in the same cluster should appear closer together. The dataset is based out of a research study, and has around 16 rows(entries) and about 50 features. I do agree that its not an ideal dataset to begin with, but unfortunately thats is the situation on hand.

Following is the approach I took:

I first applied KMeans on the dataset after normalizing it.

In parallel I also tried to use TSNE to map the data into 2 dimensions and plotted them on a scatterplot. From my understanding of TSNE, that technique should already be placing items in same clusters closer to each other. When I look at the scatterplot, however, the clusters are really all over the place.

The result of the scatterplot can be found here: https://i.sstatic.net/f4h30.jpg

Is this because TSNE and KMeans intrinsically work differently? Should I just do TSNE and try to label the clusters (and if so, how?) or should I be using TSNE output to feed into KMeans somehow?

I am really new in this space and advice would be greatly appreciated!

Thanks in advance once again

Edit: The same overlap happens if I first use TSNE to reduce dimensions to 2 and then use those reduced dimensions to cluster using KMeans

Upvotes: 1

Views: 1991

Answers (2)

Sushi Lover
Sushi Lover

Reputation: 1

Apply tSNE and fit k-means is one of the basic things you can start from. I would say consider using different f-divergence.

Stochastic Neighbor Embedding under f-divergences https://arxiv.org/pdf/1811.01247.pdf

This paper tries five different f- divergence functions : KL, RKL, JS, CH (Chi-Square), HL (Hellinger).

The paper goes over which divergence emphasize what in terms of precision and recall.

Upvotes: 0

andrewchauzov
andrewchauzov

Reputation: 1009

There is a difference between TSNE and KMeans. TSNE is used for visualization mostly and it tries to project points on the 2D/3D space (from bigger spaces) in order to keep distances (if in the bigger space 2 points were far away TSNE will try to show it).

So TSNE is not a real clustering. And that's why results you got that strange scatter plot.

For TSNE sometimes you need to apply PCA before but that is needed if your number of features is big. Just to speed-up calculations.

As already advised, try to use hierarchical clustering or simply generate more rows.

Upvotes: 2

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