As the title says I'm trying to do clustering on a set of black and white images. These images are all 200x200 with black dots on a white canvas Example pics here (These are not actual photos from the data set. Just a general idea of what they look like). The idea is to hopefully find an underlying pattern when it comes to general shape of these images and hopefully cluster by that.
What I've done so far is turned each of my 200 x 200 image in a numpy array of size 40,000. Then put all the images together into a singly numpy array of 32k x 40k.
I'm kind of not sure where to go from there. What I did next was use scikit learn's TruncateSVD on my data set and set the paramater 'n_componenets=100' and fitted and transformed it, so now my data set of images are 32k x 100.
From here on out I don't know what's the best plan of action. Should I just start using k_means algos on my data set? and how would I visualize it?
Sorry if this is a lot. Any help/tips would be much appreciated it.