2009 FrequentPatternMiningwithUncert

From GM-RKB
Jump to: navigation, search

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Abstract

This paper studies the problem of frequent pattern mining with uncertain data. We will show how broad classes of algorithms can be extended to the uncertain data setting. In particular, we will study candidate generate-and-test algorithms, hyper-structure algorithms and pattern growth algorithms. One of our insightful observations is that the experimental behavior of different classes of algorithms is very different in the uncertain case as compared to the deterministic case. In particular, the hyper-structure and the candidate generate-and-test algorithms perform much better than tree-based algorithms. This counter-intuitive behavior is an important observation from the perspective of algorithm design of the uncertain variation of the problem. We will test the approach on a number of real and synthetic data sets, and show the effectiveness of two of our approaches over competitive techniques.

Executable and Data Sets: Available at : http://dbgroup.cs.tsinghua.edu.cn/liyan/u_mining.tar.gz

References

,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 FrequentPatternMiningwithUncertCharu C. Aggarwal
Yan Li
Jianyong Wang
Jing Wang
Frequent Pattern Mining with Uncertain DataKDD-2009 Proceedings10.1145/1557019.15570302009