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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ding, Wei Eick, Christoph F. Yuan, Xiaojing Wang, Jing Nicot, Jean-Philippe |
| Copyright Year | 2007 |
| Abstract | A special challenge for spatial data mining is that information is not distributed uniformly in spatial data sets. Consequently, the discovery of regional knowledge is of fundamental importance. Unfortunately, regional patterns frequently fail to be discovered due to insuf- ficient global confidence and/or support in traditional association rule mining. Regional association rules, by definition, only hold in a subspace but not in the global space. One novel challenge is how to evaluate the impact of regional association rules. This paper centers on regional association rule scoping. We intro- duce a reward-based region discovery framework that employs clustering to find places where regional asso- ciation rules are valid. We evaluate our approach in a real-world case study to discover arsenic risk zones in the Texas water supply. The experimental results are validated by domain experts and compared with pub- lished results on arsenic contamination. |
| Starting Page | 595 |
| Ending Page | 600 |
| File Size | 281713 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769530192 |
| DOI | 10.1109/ICDMW.2007.26 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Geology Conferences Clustering algorithms Statistical distributions Water pollution Explosions Association rules Data mining Statistics Contamination |
| Content Type | Text |
| Resource Type | Article |
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