eps of core points (called boundary points)and noise when the dbscan algorithm is run on particular dataset multiple timesthe clustering of the core points is always the sameand the same points will always be labeled as noise howevera boundary point might be neighbor to core samples of more than one cluster thereforethe cluster membership of boundary points depends on the order in which points are visited usually there are only few boundary pointsand this slight dependence on the order of points is not important let' apply dbscan on the synthetic dataset we used to demonstrate agglomerative clustering like agglomerative clusteringdbscan does not allow predictions on new test dataso we will use the fit_predict method to perform clustering and return the cluster labels in one stepin[ ]from sklearn cluster import dbscan xy make_blobs(random_state= n_samples= dbscan dbscan(clusters dbscan fit_predict(xprint("cluster memberships:\ {}format(clusters)out[ ]cluster memberships[- - - - - - - - - - - - as you can seeall data points were assigned the label - which stands for noise this is consequence of the default parameter settings for eps and min_sampleswhich are not tuned for small toy datasets the cluster assignments for different values of min_samples and eps are shown belowand visualized in figure - in[ ]mglearn plots plot_dbscan(out[ ]min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps min_samples eps cluster[- - - - - cluster[ cluster[ cluster[ cluster[- - - - - cluster[ cluster[ cluster[ cluster[- - - - - - - - - - - - cluster[- - - - - - - cluster[- - - - - - - cluster[ unsupervised learning and preprocessing