ting the number of clusters to --the text to the left shows the index of the cluster and the total number of points in the cluster herethe clustering seems to have picked up on "dark skinned and smiling,"collared shirt,"smiling woman,"hussein,and "high forehead we could also find these highly similar clusters using the dendrogramif we did more detailed analysis summary of clustering methods this section has shown that applying and evaluating clustering is highly qualitative procedureand often most helpful in the exploratory phase of data analysis we looked at three clustering algorithmsk-meansdbscanand agglomerative clustering all three have way of controlling the granularity of clustering -means and agglomerative clustering allow you to specify the number of desired clusterswhile dbscan lets you define proximity using the eps parameterwhich indirectly influences cluster size all three methods can be used on largereal-world datasetsare relatively easy to understandand allow for clustering into many clusters each of the algorithms has somewhat different strengths -means allows for characterization of the clusters using the cluster means it can also be viewed as decomposition methodwhere each data point is represented by its cluster center dbscan allows for the detection of "noise pointsthat are not assigned any clusterand it can help automatically determine the number of clusters in contrast to the other two methodsit allow for complex cluster shapesas we saw in the two_moons example dbscan sometimes produces clusters of very differing sizewhich can be strength or weakness agglomerative clustering can provide whole hierarchy of possible partitions of the datawhich can be easily inspected via dendrograms clustering