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- Ke Wang, Yuelong Jiang, Alexander Tuzhilin
- 22nd International Conference on Data Engineering…
- 2006

Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user."Actionability" addresses this problem in that a pattern is deemed actionable if the user can act upon it in her favor. We introduce the notion of "action" as a domain-independent way to model the domain knowledge. Given a… (More)

- Ke Wang, Yuelong Jiang, Jeffrey Xu Yu, Guozhu Dong, Jiawei Han
- IEEE Transactions on Knowledge and Data…
- 2005

The iceberg cube mining computes all cells v, corresponding to GROUP BY partitions, that satisfy a given constraint on aggregated behaviors of the tuples in a GROUP BY partition. The number of cells often is so large that the result cannot be realistically searched without pushing the constraint into the search. Previous works have pushed antimonotone and… (More)

- Ke Wang, Yuelong Jiang, Laks V. S. Lakshmanan
- KDD
- 2003

Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues that have been previously ignored in mining unexpected rules. First, the unexpectedness of a rule depends on <i>how</i> the user prefers to apply the prior knowledge to a given… (More)

- Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wai-Chee Fu
- Fifth IEEE International Conference on Data…
- 2005

Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.

- Ke Wang, Yuelong Jiang, Jeffrey Xu Yu, Guozhu Dong, Jiawei Han
- ICDE
- 2003

Iceberg-cube mining is to compute the GROUP BY partitions, for all GROUP BY dimension lists, that satisfy a given aggregate constraint. Previous works have pushed anti-monotone constraints into iceberg-cube mining. However, many useful constraints are not anti-monotone. In this paper, we propose a novel strategy for pushing general aggregate constraints,… (More)

- Wen Jin, Yuelong Jiang, Weining Qian, Anthony K. H. Tung
- DASFAA
- 2006

Outlier analysis is an important task in data mining and has attracted much attention in both research and applications. Previous work on outlier detection involves different types of databases such as spatial databases, time series databases, biomedical databases, etc. However, few of the existing studies have considered spatial networks where points… (More)

A main goal in data mining is finding those interesting rules from data, which may help the user to do something to her advantage. In order to explore really interesting rules, numerous measures of interestingness and corresponding constraints have been developed based on the structures of rules and the statistic information of data. Given measures and… (More)

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