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SIAM Journal on Matrix Analysis and Applications
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SIAM Journal on Scientific Computing
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Journal of Parallel and Distributed Computing
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ACM Transactions on Mathematical Software (TOMS)
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ACM Transactions on Mathematical Software (TOMS)
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On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem
SIAM Journal on Matrix Analysis and Applications
Concept decompositions for large sparse text data using clustering
Machine Learning
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SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
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FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Rank degeneracy and least squares problems
Rank degeneracy and least squares problems
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
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SIAM Journal on Computing
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Approximation schemes for a class of subset selection problems
Theoretical Computer Science
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Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
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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 program-guided 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 a family of CSS algorithms with different clustering components such as k-means and Close-to-Rank-One (CRO) clustering, and Subset Selection components such as best rank-one approximation and Rank-Revealing QR (RRQR) decomposition. From an empirical perspective, we illustrate that CSS is achieving significant improvements over existing Subset Selection methods in terms of approximation errors. Compared to existing Subset Selection techniques, CSS is also able to provide additional insight about clusters and cluster representatives. Finally, we present a case-study of program-guided data explorations using CSS on a large amount of IT service delivery data collection.