clusters present[- cluster sizes[ eps= clusters present[- cluster sizes[ eps= clusters present[- cluster sizes[ eps= clusters present[- cluster sizes eps= clusters present[- cluster sizes for low settings of epsall points are labeled as noise for eps= we get many noise points and many smaller clusters for eps= we still get many noise pointsbut we get one big cluster and some smaller clusters starting from eps= we get only one large cluster and noise what is interesting to note is that there is never more than one large cluster at mostthere is one large cluster containing most of the pointsand some smaller clusters this indicates that there are not two or three different kinds of face images in the data that are very distinctbut rather that all images are more or less equally similar to (or dissimilar fromthe rest the results for eps= look most interestingwith many small clusters we can investigate this clustering in more detail by visualizing all of the points in each of the small clusters (figure - )in[ ]dbscan dbscan(min_samples= eps= labels dbscan fit_predict(x_pcafor cluster in range(max(labels )mask labels =cluster n_images np sum(maskfigaxes plt subplots( n_imagesfigsize=(n_images )subplot_kw={'xticks'()'yticks'()}for imagelabelax in zip(x_people[mask]y_people[mask]axes)ax imshow(image reshape(image_shape)vmin= vmax= ax set_title(people target_names[labelsplit()[- ] unsupervised learning and preprocessing