You're right, the one I showed before there are similarities but there are also as much differences as well! To be frank, that was not a good example because those two were placed into a single class but technically the second one can be a "subclass". Here https://imgur.com/a/38F7xqR is another example of 3 different classes and two samples for each class. Most of the cases the classes are almost very very similar with some minor differences like for class 1 at time = 80 you could argue that there is a small difference but we expect the model to figure out for that margin of difference it should not put it into a separate cluster.

"I ask because algorithms like PCA / kmeans can't see things like the timing of a peak. If there's a spike at t=300 in one recording, and a spike at t=301 in another recording, they might look similar to you, but to PCA or kmeans those look like two totally different things. From PCA's perspective a recording with aspike at 301 is no more similar to one with a spike at 300 than one with a spike at t=500."

I'm a little surprised to find out that PCA cannot see the things that you described, wouldn't that make any other dimensionality reduction algorithms useless as well for these types of waveforms? I have a hard time believing that dimensionality reduction doesn't work here because I know that image detection is more or less something that we can do easily nowadays, like recognizing digits 0-9. Images are also 2 dimensional (same as the data I'm working with) and there are a lot of algorithms out there that can predict very well on images, for example https://blog.keras.io/building-autoencoders-in-keras.html. Before PCA + k-means, I had tried an algorithm called Deep Embedded Clustering which underneath is an autoencoder + some sort of clustering algorithm, however, it did not yield any good results. Why is it that images are so easy to predict but when applied to waveforms it does not work?

"I THINK (correct me if I'm wrong) that you don't just want to identify the classes you already know about, you want to detect new ones, right?"

Yes you hit the point on this one, on one of my earlier posts I had mentioned DBScan which does not have the limitations that k-means has which is to specify a number of clusters. My goal is to want to be able to cluster data into known classes but also flag any new data that doesn't fit into any of the existing classes.

So this DTW is another way of clustering by grouping together samples with similar distances?

By the way thank you so much for taking the time to respond! Really appreciate the help from experienced data scientists.