Data mining, hypergraph transversals, and machine learning (extended abstract)

Several data mining problems can be formulated as problems of nding maximally speci c sentences that are interesting in a database. We rst show that this problem has a close relationship with the hypergraph transversal problem. We then analyze two algorithms that have been previously used in data mining, proving upper bounds on their complexity. The rst algorithm is useful when the maximally speci c interesting sentences are \small". We show that this algorithm can also be used to e ciently solve a special case of the hypergraph transversal problem, improving on previous results. The second algorithm utilizes a subroutine for hypergraph transversals, and is applicable in more general situations, with complexity close to a lower bound for the problem. We also relate these problems to the model of exact learning in computational learning theory, and use the correspondence to derive some corollaries.

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