2009 FrequentPatternMiningwithUncert

• (Aggarwal et al., 2009) ⇒ Charu C. Aggarwal, Yan Li, Jianyong Wang, and Jing Wang. (2009). “Frequent Pattern Mining with Uncertain Data.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557030

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Applications: data mining.
• General Terms: Algorithms, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Frequent+pattern+mining+with+uncertain+data%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557030&preflayout=flat#citedby

Quotes

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

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 FrequentPatternMiningwithUncert	Charu C. Aggarwal Jianyong Wang Yan Li Jing Wang			Frequent Pattern Mining with Uncertain Data		KDD-2009 Proceedings		10.1145/1557019.1557030		2009