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The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, the authors present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, they require to scan the given database a lot of times.
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