AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset

Provided by: Academy Publisher
Topic: Data Management
Format: PDF
Frequent Itemset/pattern Mining (FIM) over uncertain transaction dataset is a fundamental task in data mining. In this paper, the authors study the problem of FIM over uncertain datasets. There are two main approaches for FIM: the level-wise approach and the pattern-growth approach. The level-wise approach requires multiple scans of dataset and generates candidate itemsets. The pattern-growth approach requires a large amount of memory and computation time to process tree nodes because the current algorithms for uncertain datasets cannot create a tree as compact as the original FP-tree.

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