Patent Document ID: 9477781
Application ID: 14247377

Base Claim:
1. A computer program product for selecting cluster variables, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions to perform a method, 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; selecting, by one or more processors, a set of variables from the plurality of candidate sets of variables that produces an overall clustering solution; and saving, by one or more processors, the set of variables.

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Claim 5:
5. The computer program product of claim 1 , the method further comprising: determining, by one or more processors, a goodness measure for the approximate clustering solution; determining, by one or more processors, a goodness measure for the variables included in the approximate clustering solution; determining, by one or more processors, whether the goodness measure for the approximate clustering solution will be improved by discarding one or more variables that have lower goodness measures compared to other variables included in the approximate clustering solution; and responsive to a determination that discarding one or more variables will improve the goodness measure for the approximate clustering solution, discarding, by one or more processors, one or more variables included in the approximate clustering solution that have lower goodness measures.