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Motivated by the enormous amounts of data collected in a large IT service provider organization, this paper presents a method for quickly and automatically summarizing and extracting meaningful insights from the data. Termed Clustered Subset Selection (CSS), our method enables programguided data explorations of high-dimensional data matrices. CSS combines clustering and subset selection into a coherent and intuitive method for data analysis. In addition to a general framework, we introduce adoi:10.1145/1458082.1458162 dblp:conf/cikm/BoutsidisSA08 fatcat:xrjesqsgv5azvai6yk3r64pxlm