1. A method of outlier detection, the method comprising: identifying, by one or more processors, a plurality of subsets of CF-trees based, at least in part, on a set of CF-trees; initiating, by one or more processors, cluster feature (CF)-tree based hierarchical clustering on leaf entries of a plurality of CF-trees included in a given subset of CF-trees, wherein each CF-tree presents a respective set of local data as leaf entries; generating, by one or more processors, respective partial clustering solutions using the given subset of CF-trees, wherein clustering activity for a resulting CF-tree ceases when a threshold number of sub-clusters has been reached, and wherein a partial clustering solution includes a set of regular sub-clusters that are included in the resulting CF-tree and candidate outlier sub-clusters that contain summaries of sets of data records; generating, by one or more processors, initial regular clusters by performing hierarchical clustering using the set of regular sub-clusters; for each candidate outlier sub-cluster of a plurality of candidate outlier sub-clusters, determining, by one or more processors, a closest initial regular cluster, and a distance separating the respective candidate outlier sub-cluster and the respective closest initial regular cluster; and determining, by one or more processors, which candidate outlier sub-clusters are outlier clusters based, at least in part, on which candidate outlier sub-clusters have a computed distance to their respective closest initial regular cluster that is greater than a corresponding distance threshold associated with their respective closest initial regular cluster.