An Approximation Algorithm of Mining Frequent Itemsets From Uncertain Dataset

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Provided by: AICIT
Topic: Big Data
Format: PDF
Mining frequent itemsets from uncertain datasets is a topic in data mining. One performance bottleneck of existing algorithms is the generating and processing of the candidate itemsets for frequent itemsets; and with the decreasing of the specified minimum expected support, the situation may become worse. To address the issue, the authors propose a strategy to reduce the number of candidate itemsets at the cost of losing some accuracy by adopting a weight value to decrease the estimated expected support.
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