Abstract
Identification of outliers can lead to the discovery of unexpected, interesting, and useful knowledge. Existing methods are designed for detecting Spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define statistical tests, analyze the statistical foundation underlying our approach, design several fast algorithms to detect spatial outliers, and provide a cost model for outlier detection procedures. In addition, we provide experimental results from the application of our algorithms on a Minneapolis-St. Paul(Twin Cities) traffic dataset to show their effectiveness and usefulness.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Editors | F. Provost, R. Srikant, M. Schkolnick, D. Lee |
| Pages | 371-376 |
| Number of pages | 6 |
| State | Published - Dec 1 2001 |
| Event | Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - San Francisco, CA, United States Duration: Aug 26 2001 → Aug 29 2001 |
Other
| Other | Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) |
|---|---|
| Country/Territory | United States |
| City | San Francisco, CA |
| Period | 8/26/01 → 8/29/01 |
Keywords
- Outlier Detection
- Spatial Data Mining
- Spatial Graphs