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K-Centers. The k-Centers prototype selector tries to adapt to the graph distrubution of set T and selects graphs that are in the center of densely populated areas. First a k-means clustering procedure is applied to set T . The number of clusters to be found is equal to the number of prototypes to be selected. Once the clusters have been established, the median of each cluster is selected as a prototype. Targetsphere. The Targetsphere prototype selector ﬁrst selects a graph gc situated in the center of T .