1. A method of selecting cluster variables, the method comprising: generating, by one or more processors, a plurality of subsets of a plurality of cluster feature (CF)-trees, wherein each CF-tree represents a respective set of local data as leaf entries; collecting, by one or more processors, a plurality of variables that were used to generate a plurality of CF-trees included in a subset; generating, by one or more processors, respective approximate clustering solutions for the plurality of subsets by applying hierarchical agglomerative clustering to, at least in part, the collected variables and leaf entries of the plurality of CF-trees; selecting, by one or more processors, a plurality of candidate sets of variables with maximal goodness that are locally optimal for respective subsets based, at least in part, on the approximate clustering solutions; and selecting, by one or more processors, a set of variables from the plurality of candidate sets of variables that produces an overall clustering solution.