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The attribute reduction algorithms designed by the method of discernibility matrix have lots of repeat and unnecessary elements in the discernibility matrix, which not only cost a mass of memory space, but also waste plenty of computing time for calculating attribute reduction. In order to improve the efficiency of such attribute reduction algorithm, by considering the idea of FP tree, a novel data...
Mining frequent itemsets in data streams has became one of the hottest research topics in data mining nowadays, recent algorithms that make use of definite error bound or probabilistic error bound, have relieved the temporal-spatial complexity at some extent. However, the introduction of unwanted sub-frequent itemsets, and the changes of itemsetspsila supports, namely concept drifts, lower the efficiency...
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