1. A method for selecting representative metrics from which anomalous metrics data can be efficiently determined, the method comprising: accessing metrics datasets, wherein each metrics dataset includes data values for a respective metric indicating activity over a data network; grouping, by a processing device, the metrics datasets into clusters based on, for each of the clusters, a similarity of data values in a respective pair of datasets from the metrics datasets; determining, by the processing device, principal component datasets for the clusters, wherein, for each cluster, each principal component dataset comprises a linear combination of a respective subset of the metrics datasets; selecting, by the processing device, representative metrics based on, for each representative metric, a corresponding one of the metrics data sets having a highest contribution to one of the principal component datasets; and executing, by the processing device, an anomaly detection that is restricted to a subset of the metrics datasets corresponding to the representative metrics.