AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset
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| Title | AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset |
| Authors | |
| Abstract | Frequent itemset/pattern mining (FIM) over uncertain transaction dataset is a fundamental task in data mining. In this paper, we 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. In this paper, we propose an array based tail node tree structure (namely AT-Tree) to maintain transaction itemsets, and a pattern-growth based algorithm named AT-Mine for FIM over uncertain dataset. AT-Tree is created by two scans of dataset and it is as compact as the original FP-Tree. AT-Mine mines frequent itemsets from AT-Tree without additional scan of dataset. We evaluate our algorithm using sparse and dense datasets; the experimental results show that our algorithm has achieved better performance than the state-of-the-art FIM algorithms on uncertain transaction datasets, especially for small minimum expected support number. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2013-06-01 |
| Source | Journal of Computers Vol 8, No 6 (2013) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |