What would be your hypothesis on the reason the fields like parapsychology are different than say computer science in this context. (Bars are flipped, and large difference)

Or do you mean the colour in the plot? That is just a random colour given to each cluster.

Seems to be hard to do since you would to model the chemical process between hundreds of possible mixture and the effects of the different preparation instructions

It's like a hundred different variables. Seems people try to make their recipes just a bit unique just to be different.

But good idea, maybe an example in the plot.

This might be a good topic for a deep dive. To have a way of explaining what the differences are between clusters.

Thanks for the idea.

Good point

It's a lot of different ingredients. Think sugar, brown sugar, powered sugar. And even if I grouped then, just the number of different main ingredients would be a long list.

But I see your point.

No This is just a visualization of how similar different recipes for pie are.

This is one of the observation. It seems there are two (very) different ways to make a chocolate pie, at least in terms of ingredients.

Yeah, that one irritated me also.

Any suggestions for a better title? This one my have been too data science-y

You might be right. But some of the comments are helpful. Like the one recommending to put a legend that indicates a dot is an individual internat pie recipe. And a scale diagram on the bottom like you find on maps to indicate the distance is differences in the recipe ingredients.

Maybe I can put a circle at the top with the label "A internet Pie Recipe" to show that the circles are individual recipes.

And a scale diagram like you find on maps at the bottom, with ticks that say 'small differencess in ingredients' and 'big differencess' to indicate that the distance between circles are the amount of differences is recipe ingredients.

This is true.

Visualizations of high-dimensional data are hard to make intuitive since it attempts to plot a dataset with 768 dimensions in a 2-dimensional image. So the axes do not have a label, like in a PCA.

Honestly looking for a better way to do this

UMAP is like a PCA in that they both do Dimension Reduction. UMAP seems to scale a bit better with lots of dimensions.

Each dot is a recipe, The clusters are similar recipes.

The distance is the difference in ingredients.

>presents no data

The distances between clusters are representative of the differences in recipe ingredients, further away, the more differences.

For example, that is why Sheperd's Pie is so far away from the others. But point taken, there are no very apparent conclusions.

How could it be represented differently with data?

Honestly trying to figure this out for a client.

>what is being visualized

Maybe if I reordered the words to "Recipes for Pie clustered by ingredients"?

For example?

They seem to fall into the chicken cluster since the others don't seem the use such different ingredients that they form their own cluster, and there are more chicken pie recipes so that's why it got that label. But thank you, it's questions like these I need.

To be honest, I had trouble visualizing this concept, hence the post. The idea was the take 3700 different recipes for pie, but understand how much variation there is in terms of their ingredients.

So the algorithm will cluster recipes that have very similar ingredients and give them one color, and place the cluster in such a manner that clusters with very different ingredients are far away from each other

Sees it is differences like: 'Cream cheese crust apple pie' vs 'No crust zucchini pie'. But because I could only label the cluster with common words in the recipe title it couldn't find common enough words

Ahh, you're not trying to help , you are just opinionated

HHmm, I may need to add more backstory. Like "Euclidean distance represents ingredient similarities"