min_samples and eps parameters in this plotpoints that belong to clusters are solidwhile the noise points are shown in white core samples are shown as large markerswhile boundary points are displayed as smaller markers increasing eps (going from left to right in the figuremeans that more points will be included in cluster this makes clusters growbut might also lead to multiple clusters joining into one increasing min_samples (going from top to bottom in the figuremeans that fewer points will be core pointsand more points will be labeled as noise the parameter eps is somewhat more importantas it determines what it means for points to be "close setting eps to be very small will mean that no points are core samplesand may lead to all points being labeled as noise setting eps to be very large will result in all points forming single cluster the min_samples setting mostly determines whether points in less dense regions will be labeled as outliers or as their own clusters if you decrease min_samplesanything that would have been cluster with less than min_samples many samples will now be labeled as noise min_samples therefore determines the minimum cluster size you can see this very clearly in figure - when going from min_samples= to min_sam ples= with eps= with min_samples= there are three clustersone of four clustering