Hello Anke,
I understand you are looking for MATLAB examples for clustering-based approaches for image data. There are various algorithms for unsupervised clustering like k-means which you can use directly for this task by inputting the double values of the images.
But the most common and efficient approach for images/spectrograms is to take features from a trained model and apply k-means on top of those for clustering.
You could try using an Autoencoder which can be used to get a representation of given input data which can be an image, text, or videos.
The Autoencoder will generate meaningful features of the images, and these features can be extracted from the last layer of encoder. The last layer of encoder is a lower dimensional representation of the image which can be used in k-means for clustering the features in K groups. This way the images will be clustered into several groups.
If you have any further queries, please feel free to reply to my answer
Sincerely,
Varun
