Research Thrust
Rapid Detection of Emerging Pattern
Data mining algorithms at the Auton Lab have successfully detected new emerging patterns in various domains: Health services, Agriculture, and Manufacturing and Oil companies. Our algorithms are 10-1000 times faster than other traditional techniques. The results demonstrate significantly higher detection power with much smaller false positive rates. We have applied these algorithms in semi/fully-automated modes under supervied/unsupervised environments and for retrospective/prospective surveillance. A few algorithms for Rapid detection of emerging patterns are: WSARE, Ultra Fast SSS, and TipMon.
Paper
| Name | Actions |
|---|---|
| A Bayesian scan statistic for spatial cluster detection | show |
| A Bayesian spatial scan statistic | show |
| A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters | show |
| A Fast Multi-Resolution Method for Detection of Significant Spatial Overdensities | show |
| A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data | show |
| Bayesian Network Anomaly Pattern Detection for Disease Outbreaks | show |
| Detecting Anomalous Patterns in Pharmacy Retail Data | show |
| Detecting Significant Multidimensional Spatial Clusters | show |
| Efficient Algorithms for Non-Parametric Clustering with Clutter | show |
| Efficient Analytics for Effective Monitoring of Biomedical Security | show |
| Monitoring Food Safety by Detecting Patterns in Consumer Complaints | show |
| Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning | show |
| Rapid Detection of Significant Spatial Clusters | show |
| Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks | show |
| Summary of Biosurveillance-relevant statistical and data mining technologies | show |
| What's Strange About Recent Events | show |