Fast discovery of association rules
Advances in knowledge discovery and data mining
Some new bounds for cover-free families
Journal of Combinatorial Theory Series A
Discovering All Most Specific Sentences by Randomized Algorithms
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Group Testing Problems with Sequences in Experimental Molecular Biology
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
New constructions of superimposed codes
IEEE Transactions on Information Theory
Efficiently decodable non-adaptive group testing
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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The screening of data sets is essential to modern technology. The use of classical group testing to isolate objects that are individually positive has become the standard experimental procedure in many applied settings. Work is just beginning in applying group testing techniques to the identification of subsets of objects that are collectively positive. This paper addresses the development of probabilistic group testing methods that lead to the identification of positive combinations of objects with specific applications to data mining.