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

<p>Stochastic Neighbor Embedding under f-divergences <a href="https://arxiv.org/pdf/1811.01247.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1811.01247.pdf</a></p>

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

<p>The paper goes over which divergence emphasize what in terms of precision and recall.</p>
Visualizing clusters using TSNE
<p>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.</p>

<p>Following is the approach I took:</p>

<p>I first applied KMeans on the dataset after normalizing it.</p>

<p>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.</p>

<p>The result of the scatterplot can be found here: <a href="https://imgur.com/ZPhPjHB" rel="nofollow noreferrer">https://imgur.com/ZPhPjHB</a></p>

<p>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?</p>

<p>I am really new in this space and advice would be greatly appreciated!</p>

<p>Thanks in advance once again</p>

<p>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</p>