Abstract
Data mining can in many instances be viewed as the task of computing a representation of a theory of a model or a database. In this paper we present a randomized algorithm that can be used to compute the representation of a theory in terms of the most specific sentences of that theory. In addition to randomization, the algorithm uses a generalization of the concept of hypergraph transversal. We apply the general algorithm, for discovering maximal frequent sets in 0/1 data, and for computing minimal keys in relations. We present some empirical results on the performance of these methods on real data. We also show some complexity theoretic evidence of the hardness of these problems.
Work supported by Alexander von Humbold-Stiftung and the Academy of Finland.
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Gunopulos, D., Mannila, H., Saluja, S. (1996). Discovering all most specific sentences by randomized algorithms extended abstract. In: Afrati, F., Kolaitis, P. (eds) Database Theory — ICDT '97. ICDT 1997. Lecture Notes in Computer Science, vol 1186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62222-5_47
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DOI: https://doi.org/10.1007/3-540-62222-5_47
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